| 1 | +/a/airfield 2 | ||
| 2 | +/a/airplane_cabin 1 | ||
| 3 | +/a/airport_terminal 1 | ||
| 4 | +/a/alcove 1 | ||
| 5 | +/a/alley 2 | ||
| 6 | +/a/amphitheater 2 | ||
| 7 | +/a/amusement_arcade 1 | ||
| 8 | +/a/amusement_park 2 | ||
| 9 | +/a/apartment_building/outdoor 2 | ||
| 10 | +/a/aquarium 1 | ||
| 11 | +/a/aqueduct 2 | ||
| 12 | +/a/arcade 1 | ||
| 13 | +/a/arch 2 | ||
| 14 | +/a/archaelogical_excavation 1 | ||
| 15 | +/a/archive 1 | ||
| 16 | +/a/arena/hockey 1 | ||
| 17 | +/a/arena/performance 1 | ||
| 18 | +/a/arena/rodeo 1 | ||
| 19 | +/a/army_base 2 | ||
| 20 | +/a/art_gallery 1 | ||
| 21 | +/a/art_school 1 | ||
| 22 | +/a/art_studio 1 | ||
| 23 | +/a/artists_loft 1 | ||
| 24 | +/a/assembly_line 1 | ||
| 25 | +/a/athletic_field/outdoor 2 | ||
| 26 | +/a/atrium/public 1 | ||
| 27 | +/a/attic 1 | ||
| 28 | +/a/auditorium 1 | ||
| 29 | +/a/auto_factory 1 | ||
| 30 | +/a/auto_showroom 1 | ||
| 31 | +/b/badlands 2 | ||
| 32 | +/b/bakery/shop 1 | ||
| 33 | +/b/balcony/exterior 2 | ||
| 34 | +/b/balcony/interior 2 | ||
| 35 | +/b/ball_pit 1 | ||
| 36 | +/b/ballroom 1 | ||
| 37 | +/b/bamboo_forest 2 | ||
| 38 | +/b/bank_vault 1 | ||
| 39 | +/b/banquet_hall 1 | ||
| 40 | +/b/bar 1 | ||
| 41 | +/b/barn 2 | ||
| 42 | +/b/barndoor 2 | ||
| 43 | +/b/baseball_field 2 | ||
| 44 | +/b/basement 1 | ||
| 45 | +/b/basketball_court/indoor 1 | ||
| 46 | +/b/bathroom 1 | ||
| 47 | +/b/bazaar/indoor 1 | ||
| 48 | +/b/bazaar/outdoor 2 | ||
| 49 | +/b/beach 2 | ||
| 50 | +/b/beach_house 2 | ||
| 51 | +/b/beauty_salon 1 | ||
| 52 | +/b/bedchamber 1 | ||
| 53 | +/b/bedroom 1 | ||
| 54 | +/b/beer_garden 2 | ||
| 55 | +/b/beer_hall 1 | ||
| 56 | +/b/berth 1 | ||
| 57 | +/b/biology_laboratory 1 | ||
| 58 | +/b/boardwalk 2 | ||
| 59 | +/b/boat_deck 2 | ||
| 60 | +/b/boathouse 2 | ||
| 61 | +/b/bookstore 1 | ||
| 62 | +/b/booth/indoor 1 | ||
| 63 | +/b/botanical_garden 2 | ||
| 64 | +/b/bow_window/indoor 1 | ||
| 65 | +/b/bowling_alley 1 | ||
| 66 | +/b/boxing_ring 1 | ||
| 67 | +/b/bridge 2 | ||
| 68 | +/b/building_facade 2 | ||
| 69 | +/b/bullring 2 | ||
| 70 | +/b/burial_chamber 1 | ||
| 71 | +/b/bus_interior 1 | ||
| 72 | +/b/bus_station/indoor 2 | ||
| 73 | +/b/butchers_shop 1 | ||
| 74 | +/b/butte 2 | ||
| 75 | +/c/cabin/outdoor 2 | ||
| 76 | +/c/cafeteria 1 | ||
| 77 | +/c/campsite 2 | ||
| 78 | +/c/campus 2 | ||
| 79 | +/c/canal/natural 2 | ||
| 80 | +/c/canal/urban 2 | ||
| 81 | +/c/candy_store 1 | ||
| 82 | +/c/canyon 2 | ||
| 83 | +/c/car_interior 1 | ||
| 84 | +/c/carrousel 2 | ||
| 85 | +/c/castle 2 | ||
| 86 | +/c/catacomb 1 | ||
| 87 | +/c/cemetery 2 | ||
| 88 | +/c/chalet 2 | ||
| 89 | +/c/chemistry_lab 1 | ||
| 90 | +/c/childs_room 1 | ||
| 91 | +/c/church/indoor 1 | ||
| 92 | +/c/church/outdoor 2 | ||
| 93 | +/c/classroom 1 | ||
| 94 | +/c/clean_room 1 | ||
| 95 | +/c/cliff 2 | ||
| 96 | +/c/closet 1 | ||
| 97 | +/c/clothing_store 1 | ||
| 98 | +/c/coast 2 | ||
| 99 | +/c/cockpit 1 | ||
| 100 | +/c/coffee_shop 1 | ||
| 101 | +/c/computer_room 1 | ||
| 102 | +/c/conference_center 1 | ||
| 103 | +/c/conference_room 1 | ||
| 104 | +/c/construction_site 2 | ||
| 105 | +/c/corn_field 2 | ||
| 106 | +/c/corral 2 | ||
| 107 | +/c/corridor 1 | ||
| 108 | +/c/cottage 2 | ||
| 109 | +/c/courthouse 2 | ||
| 110 | +/c/courtyard 2 | ||
| 111 | +/c/creek 2 | ||
| 112 | +/c/crevasse 2 | ||
| 113 | +/c/crosswalk 2 | ||
| 114 | +/d/dam 2 | ||
| 115 | +/d/delicatessen 1 | ||
| 116 | +/d/department_store 1 | ||
| 117 | +/d/desert/sand 2 | ||
| 118 | +/d/desert/vegetation 2 | ||
| 119 | +/d/desert_road 2 | ||
| 120 | +/d/diner/outdoor 2 | ||
| 121 | +/d/dining_hall 1 | ||
| 122 | +/d/dining_room 1 | ||
| 123 | +/d/discotheque 1 | ||
| 124 | +/d/doorway/outdoor 2 | ||
| 125 | +/d/dorm_room 1 | ||
| 126 | +/d/downtown 2 | ||
| 127 | +/d/dressing_room 1 | ||
| 128 | +/d/driveway 2 | ||
| 129 | +/d/drugstore 1 | ||
| 130 | +/e/elevator/door 1 | ||
| 131 | +/e/elevator_lobby 1 | ||
| 132 | +/e/elevator_shaft 1 | ||
| 133 | +/e/embassy 2 | ||
| 134 | +/e/engine_room 1 | ||
| 135 | +/e/entrance_hall 1 | ||
| 136 | +/e/escalator/indoor 1 | ||
| 137 | +/e/excavation 2 | ||
| 138 | +/f/fabric_store 1 | ||
| 139 | +/f/farm 2 | ||
| 140 | +/f/fastfood_restaurant 1 | ||
| 141 | +/f/field/cultivated 2 | ||
| 142 | +/f/field/wild 2 | ||
| 143 | +/f/field_road 2 | ||
| 144 | +/f/fire_escape 2 | ||
| 145 | +/f/fire_station 2 | ||
| 146 | +/f/fishpond 2 | ||
| 147 | +/f/flea_market/indoor 1 | ||
| 148 | +/f/florist_shop/indoor 1 | ||
| 149 | +/f/food_court 1 | ||
| 150 | +/f/football_field 2 | ||
| 151 | +/f/forest/broadleaf 2 | ||
| 152 | +/f/forest_path 2 | ||
| 153 | +/f/forest_road 2 | ||
| 154 | +/f/formal_garden 2 | ||
| 155 | +/f/fountain 2 | ||
| 156 | +/g/galley 1 | ||
| 157 | +/g/garage/indoor 1 | ||
| 158 | +/g/garage/outdoor 2 | ||
| 159 | +/g/gas_station 2 | ||
| 160 | +/g/gazebo/exterior 2 | ||
| 161 | +/g/general_store/indoor 1 | ||
| 162 | +/g/general_store/outdoor 2 | ||
| 163 | +/g/gift_shop 1 | ||
| 164 | +/g/glacier 2 | ||
| 165 | +/g/golf_course 2 | ||
| 166 | +/g/greenhouse/indoor 1 | ||
| 167 | +/g/greenhouse/outdoor 2 | ||
| 168 | +/g/grotto 2 | ||
| 169 | +/g/gymnasium/indoor 1 | ||
| 170 | +/h/hangar/indoor 1 | ||
| 171 | +/h/hangar/outdoor 2 | ||
| 172 | +/h/harbor 2 | ||
| 173 | +/h/hardware_store 1 | ||
| 174 | +/h/hayfield 2 | ||
| 175 | +/h/heliport 2 | ||
| 176 | +/h/highway 2 | ||
| 177 | +/h/home_office 1 | ||
| 178 | +/h/home_theater 1 | ||
| 179 | +/h/hospital 2 | ||
| 180 | +/h/hospital_room 1 | ||
| 181 | +/h/hot_spring 2 | ||
| 182 | +/h/hotel/outdoor 2 | ||
| 183 | +/h/hotel_room 1 | ||
| 184 | +/h/house 2 | ||
| 185 | +/h/hunting_lodge/outdoor 2 | ||
| 186 | +/i/ice_cream_parlor 1 | ||
| 187 | +/i/ice_floe 2 | ||
| 188 | +/i/ice_shelf 2 | ||
| 189 | +/i/ice_skating_rink/indoor 1 | ||
| 190 | +/i/ice_skating_rink/outdoor 2 | ||
| 191 | +/i/iceberg 2 | ||
| 192 | +/i/igloo 2 | ||
| 193 | +/i/industrial_area 2 | ||
| 194 | +/i/inn/outdoor 2 | ||
| 195 | +/i/islet 2 | ||
| 196 | +/j/jacuzzi/indoor 1 | ||
| 197 | +/j/jail_cell 1 | ||
| 198 | +/j/japanese_garden 2 | ||
| 199 | +/j/jewelry_shop 1 | ||
| 200 | +/j/junkyard 2 | ||
| 201 | +/k/kasbah 2 | ||
| 202 | +/k/kennel/outdoor 2 | ||
| 203 | +/k/kindergarden_classroom 1 | ||
| 204 | +/k/kitchen 1 | ||
| 205 | +/l/lagoon 2 | ||
| 206 | +/l/lake/natural 2 | ||
| 207 | +/l/landfill 2 | ||
| 208 | +/l/landing_deck 2 | ||
| 209 | +/l/laundromat 1 | ||
| 210 | +/l/lawn 2 | ||
| 211 | +/l/lecture_room 1 | ||
| 212 | +/l/legislative_chamber 1 | ||
| 213 | +/l/library/indoor 1 | ||
| 214 | +/l/library/outdoor 2 | ||
| 215 | +/l/lighthouse 2 | ||
| 216 | +/l/living_room 1 | ||
| 217 | +/l/loading_dock 2 | ||
| 218 | +/l/lobby 1 | ||
| 219 | +/l/lock_chamber 2 | ||
| 220 | +/l/locker_room 1 | ||
| 221 | +/m/mansion 2 | ||
| 222 | +/m/manufactured_home 2 | ||
| 223 | +/m/market/indoor 1 | ||
| 224 | +/m/market/outdoor 2 | ||
| 225 | +/m/marsh 2 | ||
| 226 | +/m/martial_arts_gym 1 | ||
| 227 | +/m/mausoleum 2 | ||
| 228 | +/m/medina 2 | ||
| 229 | +/m/mezzanine 1 | ||
| 230 | +/m/moat/water 2 | ||
| 231 | +/m/mosque/outdoor 2 | ||
| 232 | +/m/motel 2 | ||
| 233 | +/m/mountain 2 | ||
| 234 | +/m/mountain_path 2 | ||
| 235 | +/m/mountain_snowy 2 | ||
| 236 | +/m/movie_theater/indoor 1 | ||
| 237 | +/m/museum/indoor 1 | ||
| 238 | +/m/museum/outdoor 2 | ||
| 239 | +/m/music_studio 1 | ||
| 240 | +/n/natural_history_museum 1 | ||
| 241 | +/n/nursery 1 | ||
| 242 | +/n/nursing_home 1 | ||
| 243 | +/o/oast_house 2 | ||
| 244 | +/o/ocean 2 | ||
| 245 | +/o/office 1 | ||
| 246 | +/o/office_building 2 | ||
| 247 | +/o/office_cubicles 1 | ||
| 248 | +/o/oilrig 2 | ||
| 249 | +/o/operating_room 1 | ||
| 250 | +/o/orchard 2 | ||
| 251 | +/o/orchestra_pit 1 | ||
| 252 | +/p/pagoda 2 | ||
| 253 | +/p/palace 2 | ||
| 254 | +/p/pantry 1 | ||
| 255 | +/p/park 2 | ||
| 256 | +/p/parking_garage/indoor 1 | ||
| 257 | +/p/parking_garage/outdoor 2 | ||
| 258 | +/p/parking_lot 2 | ||
| 259 | +/p/pasture 2 | ||
| 260 | +/p/patio 2 | ||
| 261 | +/p/pavilion 2 | ||
| 262 | +/p/pet_shop 1 | ||
| 263 | +/p/pharmacy 1 | ||
| 264 | +/p/phone_booth 2 | ||
| 265 | +/p/physics_laboratory 1 | ||
| 266 | +/p/picnic_area 2 | ||
| 267 | +/p/pier 2 | ||
| 268 | +/p/pizzeria 1 | ||
| 269 | +/p/playground 2 | ||
| 270 | +/p/playroom 1 | ||
| 271 | +/p/plaza 2 | ||
| 272 | +/p/pond 2 | ||
| 273 | +/p/porch 2 | ||
| 274 | +/p/promenade 2 | ||
| 275 | +/p/pub/indoor 1 | ||
| 276 | +/r/racecourse 2 | ||
| 277 | +/r/raceway 2 | ||
| 278 | +/r/raft 2 | ||
| 279 | +/r/railroad_track 2 | ||
| 280 | +/r/rainforest 2 | ||
| 281 | +/r/reception 1 | ||
| 282 | +/r/recreation_room 1 | ||
| 283 | +/r/repair_shop 1 | ||
| 284 | +/r/residential_neighborhood 2 | ||
| 285 | +/r/restaurant 1 | ||
| 286 | +/r/restaurant_kitchen 1 | ||
| 287 | +/r/restaurant_patio 2 | ||
| 288 | +/r/rice_paddy 2 | ||
| 289 | +/r/river 2 | ||
| 290 | +/r/rock_arch 2 | ||
| 291 | +/r/roof_garden 2 | ||
| 292 | +/r/rope_bridge 2 | ||
| 293 | +/r/ruin 2 | ||
| 294 | +/r/runway 2 | ||
| 295 | +/s/sandbox 2 | ||
| 296 | +/s/sauna 1 | ||
| 297 | +/s/schoolhouse 2 | ||
| 298 | +/s/science_museum 1 | ||
| 299 | +/s/server_room 1 | ||
| 300 | +/s/shed 2 | ||
| 301 | +/s/shoe_shop 1 | ||
| 302 | +/s/shopfront 2 | ||
| 303 | +/s/shopping_mall/indoor 1 | ||
| 304 | +/s/shower 1 | ||
| 305 | +/s/ski_resort 2 | ||
| 306 | +/s/ski_slope 2 | ||
| 307 | +/s/sky 2 | ||
| 308 | +/s/skyscraper 2 | ||
| 309 | +/s/slum 2 | ||
| 310 | +/s/snowfield 2 | ||
| 311 | +/s/soccer_field 2 | ||
| 312 | +/s/stable 1 | ||
| 313 | +/s/stadium/baseball 2 | ||
| 314 | +/s/stadium/football 2 | ||
| 315 | +/s/stadium/soccer 2 | ||
| 316 | +/s/stage/indoor 1 | ||
| 317 | +/s/stage/outdoor 2 | ||
| 318 | +/s/staircase 1 | ||
| 319 | +/s/storage_room 1 | ||
| 320 | +/s/street 2 | ||
| 321 | +/s/subway_station/platform 1 | ||
| 322 | +/s/supermarket 1 | ||
| 323 | +/s/sushi_bar 1 | ||
| 324 | +/s/swamp 2 | ||
| 325 | +/s/swimming_hole 1 | ||
| 326 | +/s/swimming_pool/indoor 1 | ||
| 327 | +/s/swimming_pool/outdoor 2 | ||
| 328 | +/s/synagogue/outdoor 2 | ||
| 329 | +/t/television_room 1 | ||
| 330 | +/t/television_studio 1 | ||
| 331 | +/t/temple/asia 2 | ||
| 332 | +/t/throne_room 1 | ||
| 333 | +/t/ticket_booth 1 | ||
| 334 | +/t/topiary_garden 2 | ||
| 335 | +/t/tower 2 | ||
| 336 | +/t/toyshop 1 | ||
| 337 | +/t/train_interior 1 | ||
| 338 | +/t/train_station/platform 1 | ||
| 339 | +/t/tree_farm 2 | ||
| 340 | +/t/tree_house 2 | ||
| 341 | +/t/trench 2 | ||
| 342 | +/t/tundra 2 | ||
| 343 | +/u/underwater/ocean_deep 2 | ||
| 344 | +/u/utility_room 1 | ||
| 345 | +/v/valley 2 | ||
| 346 | +/v/vegetable_garden 2 | ||
| 347 | +/v/veterinarians_office 1 | ||
| 348 | +/v/viaduct 2 | ||
| 349 | +/v/village 2 | ||
| 350 | +/v/vineyard 2 | ||
| 351 | +/v/volcano 2 | ||
| 352 | +/v/volleyball_court/outdoor 2 | ||
| 353 | +/w/waiting_room 1 | ||
| 354 | +/w/water_park 2 | ||
| 355 | +/w/water_tower 2 | ||
| 356 | +/w/waterfall 2 | ||
| 357 | +/w/watering_hole 2 | ||
| 358 | +/w/wave 2 | ||
| 359 | +/w/wet_bar 1 | ||
| 360 | +/w/wheat_field 2 | ||
| 361 | +/w/wind_farm 2 | ||
| 362 | +/w/windmill 2 | ||
| 363 | +/y/yard 2 | ||
| 364 | +/y/youth_hostel 1 | ||
| 365 | +/z/zen_garden 2 |
| 1 | +/a/airfield 0 | ||
| 2 | +/a/airplane_cabin 1 | ||
| 3 | +/a/airport_terminal 2 | ||
| 4 | +/a/alcove 3 | ||
| 5 | +/a/alley 4 | ||
| 6 | +/a/amphitheater 5 | ||
| 7 | +/a/amusement_arcade 6 | ||
| 8 | +/a/amusement_park 7 | ||
| 9 | +/a/apartment_building/outdoor 8 | ||
| 10 | +/a/aquarium 9 | ||
| 11 | +/a/aqueduct 10 | ||
| 12 | +/a/arcade 11 | ||
| 13 | +/a/arch 12 | ||
| 14 | +/a/archaelogical_excavation 13 | ||
| 15 | +/a/archive 14 | ||
| 16 | +/a/arena/hockey 15 | ||
| 17 | +/a/arena/performance 16 | ||
| 18 | +/a/arena/rodeo 17 | ||
| 19 | +/a/army_base 18 | ||
| 20 | +/a/art_gallery 19 | ||
| 21 | +/a/art_school 20 | ||
| 22 | +/a/art_studio 21 | ||
| 23 | +/a/artists_loft 22 | ||
| 24 | +/a/assembly_line 23 | ||
| 25 | +/a/athletic_field/outdoor 24 | ||
| 26 | +/a/atrium/public 25 | ||
| 27 | +/a/attic 26 | ||
| 28 | +/a/auditorium 27 | ||
| 29 | +/a/auto_factory 28 | ||
| 30 | +/a/auto_showroom 29 | ||
| 31 | +/b/badlands 30 | ||
| 32 | +/b/bakery/shop 31 | ||
| 33 | +/b/balcony/exterior 32 | ||
| 34 | +/b/balcony/interior 33 | ||
| 35 | +/b/ball_pit 34 | ||
| 36 | +/b/ballroom 35 | ||
| 37 | +/b/bamboo_forest 36 | ||
| 38 | +/b/bank_vault 37 | ||
| 39 | +/b/banquet_hall 38 | ||
| 40 | +/b/bar 39 | ||
| 41 | +/b/barn 40 | ||
| 42 | +/b/barndoor 41 | ||
| 43 | +/b/baseball_field 42 | ||
| 44 | +/b/basement 43 | ||
| 45 | +/b/basketball_court/indoor 44 | ||
| 46 | +/b/bathroom 45 | ||
| 47 | +/b/bazaar/indoor 46 | ||
| 48 | +/b/bazaar/outdoor 47 | ||
| 49 | +/b/beach 48 | ||
| 50 | +/b/beach_house 49 | ||
| 51 | +/b/beauty_salon 50 | ||
| 52 | +/b/bedchamber 51 | ||
| 53 | +/b/bedroom 52 | ||
| 54 | +/b/beer_garden 53 | ||
| 55 | +/b/beer_hall 54 | ||
| 56 | +/b/berth 55 | ||
| 57 | +/b/biology_laboratory 56 | ||
| 58 | +/b/boardwalk 57 | ||
| 59 | +/b/boat_deck 58 | ||
| 60 | +/b/boathouse 59 | ||
| 61 | +/b/bookstore 60 | ||
| 62 | +/b/booth/indoor 61 | ||
| 63 | +/b/botanical_garden 62 | ||
| 64 | +/b/bow_window/indoor 63 | ||
| 65 | +/b/bowling_alley 64 | ||
| 66 | +/b/boxing_ring 65 | ||
| 67 | +/b/bridge 66 | ||
| 68 | +/b/building_facade 67 | ||
| 69 | +/b/bullring 68 | ||
| 70 | +/b/burial_chamber 69 | ||
| 71 | +/b/bus_interior 70 | ||
| 72 | +/b/bus_station/indoor 71 | ||
| 73 | +/b/butchers_shop 72 | ||
| 74 | +/b/butte 73 | ||
| 75 | +/c/cabin/outdoor 74 | ||
| 76 | +/c/cafeteria 75 | ||
| 77 | +/c/campsite 76 | ||
| 78 | +/c/campus 77 | ||
| 79 | +/c/canal/natural 78 | ||
| 80 | +/c/canal/urban 79 | ||
| 81 | +/c/candy_store 80 | ||
| 82 | +/c/canyon 81 | ||
| 83 | +/c/car_interior 82 | ||
| 84 | +/c/carrousel 83 | ||
| 85 | +/c/castle 84 | ||
| 86 | +/c/catacomb 85 | ||
| 87 | +/c/cemetery 86 | ||
| 88 | +/c/chalet 87 | ||
| 89 | +/c/chemistry_lab 88 | ||
| 90 | +/c/childs_room 89 | ||
| 91 | +/c/church/indoor 90 | ||
| 92 | +/c/church/outdoor 91 | ||
| 93 | +/c/classroom 92 | ||
| 94 | +/c/clean_room 93 | ||
| 95 | +/c/cliff 94 | ||
| 96 | +/c/closet 95 | ||
| 97 | +/c/clothing_store 96 | ||
| 98 | +/c/coast 97 | ||
| 99 | +/c/cockpit 98 | ||
| 100 | +/c/coffee_shop 99 | ||
| 101 | +/c/computer_room 100 | ||
| 102 | +/c/conference_center 101 | ||
| 103 | +/c/conference_room 102 | ||
| 104 | +/c/construction_site 103 | ||
| 105 | +/c/corn_field 104 | ||
| 106 | +/c/corral 105 | ||
| 107 | +/c/corridor 106 | ||
| 108 | +/c/cottage 107 | ||
| 109 | +/c/courthouse 108 | ||
| 110 | +/c/courtyard 109 | ||
| 111 | +/c/creek 110 | ||
| 112 | +/c/crevasse 111 | ||
| 113 | +/c/crosswalk 112 | ||
| 114 | +/d/dam 113 | ||
| 115 | +/d/delicatessen 114 | ||
| 116 | +/d/department_store 115 | ||
| 117 | +/d/desert/sand 116 | ||
| 118 | +/d/desert/vegetation 117 | ||
| 119 | +/d/desert_road 118 | ||
| 120 | +/d/diner/outdoor 119 | ||
| 121 | +/d/dining_hall 120 | ||
| 122 | +/d/dining_room 121 | ||
| 123 | +/d/discotheque 122 | ||
| 124 | +/d/doorway/outdoor 123 | ||
| 125 | +/d/dorm_room 124 | ||
| 126 | +/d/downtown 125 | ||
| 127 | +/d/dressing_room 126 | ||
| 128 | +/d/driveway 127 | ||
| 129 | +/d/drugstore 128 | ||
| 130 | +/e/elevator/door 129 | ||
| 131 | +/e/elevator_lobby 130 | ||
| 132 | +/e/elevator_shaft 131 | ||
| 133 | +/e/embassy 132 | ||
| 134 | +/e/engine_room 133 | ||
| 135 | +/e/entrance_hall 134 | ||
| 136 | +/e/escalator/indoor 135 | ||
| 137 | +/e/excavation 136 | ||
| 138 | +/f/fabric_store 137 | ||
| 139 | +/f/farm 138 | ||
| 140 | +/f/fastfood_restaurant 139 | ||
| 141 | +/f/field/cultivated 140 | ||
| 142 | +/f/field/wild 141 | ||
| 143 | +/f/field_road 142 | ||
| 144 | +/f/fire_escape 143 | ||
| 145 | +/f/fire_station 144 | ||
| 146 | +/f/fishpond 145 | ||
| 147 | +/f/flea_market/indoor 146 | ||
| 148 | +/f/florist_shop/indoor 147 | ||
| 149 | +/f/food_court 148 | ||
| 150 | +/f/football_field 149 | ||
| 151 | +/f/forest/broadleaf 150 | ||
| 152 | +/f/forest_path 151 | ||
| 153 | +/f/forest_road 152 | ||
| 154 | +/f/formal_garden 153 | ||
| 155 | +/f/fountain 154 | ||
| 156 | +/g/galley 155 | ||
| 157 | +/g/garage/indoor 156 | ||
| 158 | +/g/garage/outdoor 157 | ||
| 159 | +/g/gas_station 158 | ||
| 160 | +/g/gazebo/exterior 159 | ||
| 161 | +/g/general_store/indoor 160 | ||
| 162 | +/g/general_store/outdoor 161 | ||
| 163 | +/g/gift_shop 162 | ||
| 164 | +/g/glacier 163 | ||
| 165 | +/g/golf_course 164 | ||
| 166 | +/g/greenhouse/indoor 165 | ||
| 167 | +/g/greenhouse/outdoor 166 | ||
| 168 | +/g/grotto 167 | ||
| 169 | +/g/gymnasium/indoor 168 | ||
| 170 | +/h/hangar/indoor 169 | ||
| 171 | +/h/hangar/outdoor 170 | ||
| 172 | +/h/harbor 171 | ||
| 173 | +/h/hardware_store 172 | ||
| 174 | +/h/hayfield 173 | ||
| 175 | +/h/heliport 174 | ||
| 176 | +/h/highway 175 | ||
| 177 | +/h/home_office 176 | ||
| 178 | +/h/home_theater 177 | ||
| 179 | +/h/hospital 178 | ||
| 180 | +/h/hospital_room 179 | ||
| 181 | +/h/hot_spring 180 | ||
| 182 | +/h/hotel/outdoor 181 | ||
| 183 | +/h/hotel_room 182 | ||
| 184 | +/h/house 183 | ||
| 185 | +/h/hunting_lodge/outdoor 184 | ||
| 186 | +/i/ice_cream_parlor 185 | ||
| 187 | +/i/ice_floe 186 | ||
| 188 | +/i/ice_shelf 187 | ||
| 189 | +/i/ice_skating_rink/indoor 188 | ||
| 190 | +/i/ice_skating_rink/outdoor 189 | ||
| 191 | +/i/iceberg 190 | ||
| 192 | +/i/igloo 191 | ||
| 193 | +/i/industrial_area 192 | ||
| 194 | +/i/inn/outdoor 193 | ||
| 195 | +/i/islet 194 | ||
| 196 | +/j/jacuzzi/indoor 195 | ||
| 197 | +/j/jail_cell 196 | ||
| 198 | +/j/japanese_garden 197 | ||
| 199 | +/j/jewelry_shop 198 | ||
| 200 | +/j/junkyard 199 | ||
| 201 | +/k/kasbah 200 | ||
| 202 | +/k/kennel/outdoor 201 | ||
| 203 | +/k/kindergarden_classroom 202 | ||
| 204 | +/k/kitchen 203 | ||
| 205 | +/l/lagoon 204 | ||
| 206 | +/l/lake/natural 205 | ||
| 207 | +/l/landfill 206 | ||
| 208 | +/l/landing_deck 207 | ||
| 209 | +/l/laundromat 208 | ||
| 210 | +/l/lawn 209 | ||
| 211 | +/l/lecture_room 210 | ||
| 212 | +/l/legislative_chamber 211 | ||
| 213 | +/l/library/indoor 212 | ||
| 214 | +/l/library/outdoor 213 | ||
| 215 | +/l/lighthouse 214 | ||
| 216 | +/l/living_room 215 | ||
| 217 | +/l/loading_dock 216 | ||
| 218 | +/l/lobby 217 | ||
| 219 | +/l/lock_chamber 218 | ||
| 220 | +/l/locker_room 219 | ||
| 221 | +/m/mansion 220 | ||
| 222 | +/m/manufactured_home 221 | ||
| 223 | +/m/market/indoor 222 | ||
| 224 | +/m/market/outdoor 223 | ||
| 225 | +/m/marsh 224 | ||
| 226 | +/m/martial_arts_gym 225 | ||
| 227 | +/m/mausoleum 226 | ||
| 228 | +/m/medina 227 | ||
| 229 | +/m/mezzanine 228 | ||
| 230 | +/m/moat/water 229 | ||
| 231 | +/m/mosque/outdoor 230 | ||
| 232 | +/m/motel 231 | ||
| 233 | +/m/mountain 232 | ||
| 234 | +/m/mountain_path 233 | ||
| 235 | +/m/mountain_snowy 234 | ||
| 236 | +/m/movie_theater/indoor 235 | ||
| 237 | +/m/museum/indoor 236 | ||
| 238 | +/m/museum/outdoor 237 | ||
| 239 | +/m/music_studio 238 | ||
| 240 | +/n/natural_history_museum 239 | ||
| 241 | +/n/nursery 240 | ||
| 242 | +/n/nursing_home 241 | ||
| 243 | +/o/oast_house 242 | ||
| 244 | +/o/ocean 243 | ||
| 245 | +/o/office 244 | ||
| 246 | +/o/office_building 245 | ||
| 247 | +/o/office_cubicles 246 | ||
| 248 | +/o/oilrig 247 | ||
| 249 | +/o/operating_room 248 | ||
| 250 | +/o/orchard 249 | ||
| 251 | +/o/orchestra_pit 250 | ||
| 252 | +/p/pagoda 251 | ||
| 253 | +/p/palace 252 | ||
| 254 | +/p/pantry 253 | ||
| 255 | +/p/park 254 | ||
| 256 | +/p/parking_garage/indoor 255 | ||
| 257 | +/p/parking_garage/outdoor 256 | ||
| 258 | +/p/parking_lot 257 | ||
| 259 | +/p/pasture 258 | ||
| 260 | +/p/patio 259 | ||
| 261 | +/p/pavilion 260 | ||
| 262 | +/p/pet_shop 261 | ||
| 263 | +/p/pharmacy 262 | ||
| 264 | +/p/phone_booth 263 | ||
| 265 | +/p/physics_laboratory 264 | ||
| 266 | +/p/picnic_area 265 | ||
| 267 | +/p/pier 266 | ||
| 268 | +/p/pizzeria 267 | ||
| 269 | +/p/playground 268 | ||
| 270 | +/p/playroom 269 | ||
| 271 | +/p/plaza 270 | ||
| 272 | +/p/pond 271 | ||
| 273 | +/p/porch 272 | ||
| 274 | +/p/promenade 273 | ||
| 275 | +/p/pub/indoor 274 | ||
| 276 | +/r/racecourse 275 | ||
| 277 | +/r/raceway 276 | ||
| 278 | +/r/raft 277 | ||
| 279 | +/r/railroad_track 278 | ||
| 280 | +/r/rainforest 279 | ||
| 281 | +/r/reception 280 | ||
| 282 | +/r/recreation_room 281 | ||
| 283 | +/r/repair_shop 282 | ||
| 284 | +/r/residential_neighborhood 283 | ||
| 285 | +/r/restaurant 284 | ||
| 286 | +/r/restaurant_kitchen 285 | ||
| 287 | +/r/restaurant_patio 286 | ||
| 288 | +/r/rice_paddy 287 | ||
| 289 | +/r/river 288 | ||
| 290 | +/r/rock_arch 289 | ||
| 291 | +/r/roof_garden 290 | ||
| 292 | +/r/rope_bridge 291 | ||
| 293 | +/r/ruin 292 | ||
| 294 | +/r/runway 293 | ||
| 295 | +/s/sandbox 294 | ||
| 296 | +/s/sauna 295 | ||
| 297 | +/s/schoolhouse 296 | ||
| 298 | +/s/science_museum 297 | ||
| 299 | +/s/server_room 298 | ||
| 300 | +/s/shed 299 | ||
| 301 | +/s/shoe_shop 300 | ||
| 302 | +/s/shopfront 301 | ||
| 303 | +/s/shopping_mall/indoor 302 | ||
| 304 | +/s/shower 303 | ||
| 305 | +/s/ski_resort 304 | ||
| 306 | +/s/ski_slope 305 | ||
| 307 | +/s/sky 306 | ||
| 308 | +/s/skyscraper 307 | ||
| 309 | +/s/slum 308 | ||
| 310 | +/s/snowfield 309 | ||
| 311 | +/s/soccer_field 310 | ||
| 312 | +/s/stable 311 | ||
| 313 | +/s/stadium/baseball 312 | ||
| 314 | +/s/stadium/football 313 | ||
| 315 | +/s/stadium/soccer 314 | ||
| 316 | +/s/stage/indoor 315 | ||
| 317 | +/s/stage/outdoor 316 | ||
| 318 | +/s/staircase 317 | ||
| 319 | +/s/storage_room 318 | ||
| 320 | +/s/street 319 | ||
| 321 | +/s/subway_station/platform 320 | ||
| 322 | +/s/supermarket 321 | ||
| 323 | +/s/sushi_bar 322 | ||
| 324 | +/s/swamp 323 | ||
| 325 | +/s/swimming_hole 324 | ||
| 326 | +/s/swimming_pool/indoor 325 | ||
| 327 | +/s/swimming_pool/outdoor 326 | ||
| 328 | +/s/synagogue/outdoor 327 | ||
| 329 | +/t/television_room 328 | ||
| 330 | +/t/television_studio 329 | ||
| 331 | +/t/temple/asia 330 | ||
| 332 | +/t/throne_room 331 | ||
| 333 | +/t/ticket_booth 332 | ||
| 334 | +/t/topiary_garden 333 | ||
| 335 | +/t/tower 334 | ||
| 336 | +/t/toyshop 335 | ||
| 337 | +/t/train_interior 336 | ||
| 338 | +/t/train_station/platform 337 | ||
| 339 | +/t/tree_farm 338 | ||
| 340 | +/t/tree_house 339 | ||
| 341 | +/t/trench 340 | ||
| 342 | +/t/tundra 341 | ||
| 343 | +/u/underwater/ocean_deep 342 | ||
| 344 | +/u/utility_room 343 | ||
| 345 | +/v/valley 344 | ||
| 346 | +/v/vegetable_garden 345 | ||
| 347 | +/v/veterinarians_office 346 | ||
| 348 | +/v/viaduct 347 | ||
| 349 | +/v/village 348 | ||
| 350 | +/v/vineyard 349 | ||
| 351 | +/v/volcano 350 | ||
| 352 | +/v/volleyball_court/outdoor 351 | ||
| 353 | +/w/waiting_room 352 | ||
| 354 | +/w/water_park 353 | ||
| 355 | +/w/water_tower 354 | ||
| 356 | +/w/waterfall 355 | ||
| 357 | +/w/watering_hole 356 | ||
| 358 | +/w/wave 357 | ||
| 359 | +/w/wet_bar 358 | ||
| 360 | +/w/wheat_field 359 | ||
| 361 | +/w/wind_farm 360 | ||
| 362 | +/w/windmill 361 | ||
| 363 | +/y/yard 362 | ||
| 364 | +/y/youth_hostel 363 | ||
| 365 | +/z/zen_garden 364 | ||
| ... | \ No newline at end of file | ... | \ No newline at end of file |
| 1 | +[net] | ||
| 2 | +# Testing | ||
| 3 | +#batch=1 | ||
| 4 | +#subdivisions=1 | ||
| 5 | +# Training | ||
| 6 | +batch=64 | ||
| 7 | +subdivisions=16 | ||
| 8 | +width=512 | ||
| 9 | +height=512 | ||
| 10 | +channels=3 | ||
| 11 | +momentum=0.9 | ||
| 12 | +decay=0.0005 | ||
| 13 | +angle=0 | ||
| 14 | +saturation = 1.5 | ||
| 15 | +exposure = 1.5 | ||
| 16 | +hue=.1 | ||
| 17 | + | ||
| 18 | +learning_rate=0.0001 | ||
| 19 | +burn_in=1000 | ||
| 20 | +max_batches = 500200 | ||
| 21 | +policy=steps | ||
| 22 | +steps=400000,450000 | ||
| 23 | +scales=.1,.1 | ||
| 24 | +max_epochs = 300 | ||
| 25 | + | ||
| 26 | +[convolutional] | ||
| 27 | +batch_normalize=1 | ||
| 28 | +filters=32 | ||
| 29 | +size=3 | ||
| 30 | +stride=1 | ||
| 31 | +pad=1 | ||
| 32 | +activation=leaky | ||
| 33 | + | ||
| 34 | +# Downsample | ||
| 35 | + | ||
| 36 | +[convolutional] | ||
| 37 | +batch_normalize=1 | ||
| 38 | +filters=64 | ||
| 39 | +size=3 | ||
| 40 | +stride=2 | ||
| 41 | +pad=1 | ||
| 42 | +activation=leaky | ||
| 43 | + | ||
| 44 | +[convolutional] | ||
| 45 | +batch_normalize=1 | ||
| 46 | +filters=32 | ||
| 47 | +size=1 | ||
| 48 | +stride=1 | ||
| 49 | +pad=1 | ||
| 50 | +activation=leaky | ||
| 51 | + | ||
| 52 | +[convolutional] | ||
| 53 | +batch_normalize=1 | ||
| 54 | +filters=64 | ||
| 55 | +size=3 | ||
| 56 | +stride=1 | ||
| 57 | +pad=1 | ||
| 58 | +activation=leaky | ||
| 59 | + | ||
| 60 | +[shortcut] | ||
| 61 | +from=-3 | ||
| 62 | +activation=linear | ||
| 63 | + | ||
| 64 | +# Downsample | ||
| 65 | + | ||
| 66 | +[convolutional] | ||
| 67 | +batch_normalize=1 | ||
| 68 | +filters=128 | ||
| 69 | +size=3 | ||
| 70 | +stride=2 | ||
| 71 | +pad=1 | ||
| 72 | +activation=leaky | ||
| 73 | + | ||
| 74 | +[convolutional] | ||
| 75 | +batch_normalize=1 | ||
| 76 | +filters=64 | ||
| 77 | +size=1 | ||
| 78 | +stride=1 | ||
| 79 | +pad=1 | ||
| 80 | +activation=leaky | ||
| 81 | + | ||
| 82 | +[convolutional] | ||
| 83 | +batch_normalize=1 | ||
| 84 | +filters=128 | ||
| 85 | +size=3 | ||
| 86 | +stride=1 | ||
| 87 | +pad=1 | ||
| 88 | +activation=leaky | ||
| 89 | + | ||
| 90 | +[shortcut] | ||
| 91 | +from=-3 | ||
| 92 | +activation=linear | ||
| 93 | + | ||
| 94 | +[convolutional] | ||
| 95 | +batch_normalize=1 | ||
| 96 | +filters=64 | ||
| 97 | +size=1 | ||
| 98 | +stride=1 | ||
| 99 | +pad=1 | ||
| 100 | +activation=leaky | ||
| 101 | + | ||
| 102 | +[convolutional] | ||
| 103 | +batch_normalize=1 | ||
| 104 | +filters=128 | ||
| 105 | +size=3 | ||
| 106 | +stride=1 | ||
| 107 | +pad=1 | ||
| 108 | +activation=leaky | ||
| 109 | + | ||
| 110 | +[shortcut] | ||
| 111 | +from=-3 | ||
| 112 | +activation=linear | ||
| 113 | + | ||
| 114 | +# Downsample | ||
| 115 | + | ||
| 116 | +[convolutional] | ||
| 117 | +batch_normalize=1 | ||
| 118 | +filters=256 | ||
| 119 | +size=3 | ||
| 120 | +stride=2 | ||
| 121 | +pad=1 | ||
| 122 | +activation=leaky | ||
| 123 | + | ||
| 124 | +[convolutional] | ||
| 125 | +batch_normalize=1 | ||
| 126 | +filters=128 | ||
| 127 | +size=1 | ||
| 128 | +stride=1 | ||
| 129 | +pad=1 | ||
| 130 | +activation=leaky | ||
| 131 | + | ||
| 132 | +[convolutional] | ||
| 133 | +batch_normalize=1 | ||
| 134 | +filters=256 | ||
| 135 | +size=3 | ||
| 136 | +stride=1 | ||
| 137 | +pad=1 | ||
| 138 | +activation=leaky | ||
| 139 | + | ||
| 140 | +[shortcut] | ||
| 141 | +from=-3 | ||
| 142 | +activation=linear | ||
| 143 | + | ||
| 144 | +[convolutional] | ||
| 145 | +batch_normalize=1 | ||
| 146 | +filters=128 | ||
| 147 | +size=1 | ||
| 148 | +stride=1 | ||
| 149 | +pad=1 | ||
| 150 | +activation=leaky | ||
| 151 | + | ||
| 152 | +[convolutional] | ||
| 153 | +batch_normalize=1 | ||
| 154 | +filters=256 | ||
| 155 | +size=3 | ||
| 156 | +stride=1 | ||
| 157 | +pad=1 | ||
| 158 | +activation=leaky | ||
| 159 | + | ||
| 160 | +[shortcut] | ||
| 161 | +from=-3 | ||
| 162 | +activation=linear | ||
| 163 | + | ||
| 164 | +[convolutional] | ||
| 165 | +batch_normalize=1 | ||
| 166 | +filters=128 | ||
| 167 | +size=1 | ||
| 168 | +stride=1 | ||
| 169 | +pad=1 | ||
| 170 | +activation=leaky | ||
| 171 | + | ||
| 172 | +[convolutional] | ||
| 173 | +batch_normalize=1 | ||
| 174 | +filters=256 | ||
| 175 | +size=3 | ||
| 176 | +stride=1 | ||
| 177 | +pad=1 | ||
| 178 | +activation=leaky | ||
| 179 | + | ||
| 180 | +[shortcut] | ||
| 181 | +from=-3 | ||
| 182 | +activation=linear | ||
| 183 | + | ||
| 184 | +[convolutional] | ||
| 185 | +batch_normalize=1 | ||
| 186 | +filters=128 | ||
| 187 | +size=1 | ||
| 188 | +stride=1 | ||
| 189 | +pad=1 | ||
| 190 | +activation=leaky | ||
| 191 | + | ||
| 192 | +[convolutional] | ||
| 193 | +batch_normalize=1 | ||
| 194 | +filters=256 | ||
| 195 | +size=3 | ||
| 196 | +stride=1 | ||
| 197 | +pad=1 | ||
| 198 | +activation=leaky | ||
| 199 | + | ||
| 200 | +[shortcut] | ||
| 201 | +from=-3 | ||
| 202 | +activation=linear | ||
| 203 | + | ||
| 204 | + | ||
| 205 | +[convolutional] | ||
| 206 | +batch_normalize=1 | ||
| 207 | +filters=128 | ||
| 208 | +size=1 | ||
| 209 | +stride=1 | ||
| 210 | +pad=1 | ||
| 211 | +activation=leaky | ||
| 212 | + | ||
| 213 | +[convolutional] | ||
| 214 | +batch_normalize=1 | ||
| 215 | +filters=256 | ||
| 216 | +size=3 | ||
| 217 | +stride=1 | ||
| 218 | +pad=1 | ||
| 219 | +activation=leaky | ||
| 220 | + | ||
| 221 | +[shortcut] | ||
| 222 | +from=-3 | ||
| 223 | +activation=linear | ||
| 224 | + | ||
| 225 | +[convolutional] | ||
| 226 | +batch_normalize=1 | ||
| 227 | +filters=128 | ||
| 228 | +size=1 | ||
| 229 | +stride=1 | ||
| 230 | +pad=1 | ||
| 231 | +activation=leaky | ||
| 232 | + | ||
| 233 | +[convolutional] | ||
| 234 | +batch_normalize=1 | ||
| 235 | +filters=256 | ||
| 236 | +size=3 | ||
| 237 | +stride=1 | ||
| 238 | +pad=1 | ||
| 239 | +activation=leaky | ||
| 240 | + | ||
| 241 | +[shortcut] | ||
| 242 | +from=-3 | ||
| 243 | +activation=linear | ||
| 244 | + | ||
| 245 | +[convolutional] | ||
| 246 | +batch_normalize=1 | ||
| 247 | +filters=128 | ||
| 248 | +size=1 | ||
| 249 | +stride=1 | ||
| 250 | +pad=1 | ||
| 251 | +activation=leaky | ||
| 252 | + | ||
| 253 | +[convolutional] | ||
| 254 | +batch_normalize=1 | ||
| 255 | +filters=256 | ||
| 256 | +size=3 | ||
| 257 | +stride=1 | ||
| 258 | +pad=1 | ||
| 259 | +activation=leaky | ||
| 260 | + | ||
| 261 | +[shortcut] | ||
| 262 | +from=-3 | ||
| 263 | +activation=linear | ||
| 264 | + | ||
| 265 | +[convolutional] | ||
| 266 | +batch_normalize=1 | ||
| 267 | +filters=128 | ||
| 268 | +size=1 | ||
| 269 | +stride=1 | ||
| 270 | +pad=1 | ||
| 271 | +activation=leaky | ||
| 272 | + | ||
| 273 | +[convolutional] | ||
| 274 | +batch_normalize=1 | ||
| 275 | +filters=256 | ||
| 276 | +size=3 | ||
| 277 | +stride=1 | ||
| 278 | +pad=1 | ||
| 279 | +activation=leaky | ||
| 280 | + | ||
| 281 | +[shortcut] | ||
| 282 | +from=-3 | ||
| 283 | +activation=linear | ||
| 284 | + | ||
| 285 | +# Downsample | ||
| 286 | + | ||
| 287 | +[convolutional] | ||
| 288 | +batch_normalize=1 | ||
| 289 | +filters=512 | ||
| 290 | +size=3 | ||
| 291 | +stride=2 | ||
| 292 | +pad=1 | ||
| 293 | +activation=leaky | ||
| 294 | + | ||
| 295 | +[convolutional] | ||
| 296 | +batch_normalize=1 | ||
| 297 | +filters=256 | ||
| 298 | +size=1 | ||
| 299 | +stride=1 | ||
| 300 | +pad=1 | ||
| 301 | +activation=leaky | ||
| 302 | + | ||
| 303 | +[convolutional] | ||
| 304 | +batch_normalize=1 | ||
| 305 | +filters=512 | ||
| 306 | +size=3 | ||
| 307 | +stride=1 | ||
| 308 | +pad=1 | ||
| 309 | +activation=leaky | ||
| 310 | + | ||
| 311 | +[shortcut] | ||
| 312 | +from=-3 | ||
| 313 | +activation=linear | ||
| 314 | + | ||
| 315 | + | ||
| 316 | +[convolutional] | ||
| 317 | +batch_normalize=1 | ||
| 318 | +filters=256 | ||
| 319 | +size=1 | ||
| 320 | +stride=1 | ||
| 321 | +pad=1 | ||
| 322 | +activation=leaky | ||
| 323 | + | ||
| 324 | +[convolutional] | ||
| 325 | +batch_normalize=1 | ||
| 326 | +filters=512 | ||
| 327 | +size=3 | ||
| 328 | +stride=1 | ||
| 329 | +pad=1 | ||
| 330 | +activation=leaky | ||
| 331 | + | ||
| 332 | +[shortcut] | ||
| 333 | +from=-3 | ||
| 334 | +activation=linear | ||
| 335 | + | ||
| 336 | + | ||
| 337 | +[convolutional] | ||
| 338 | +batch_normalize=1 | ||
| 339 | +filters=256 | ||
| 340 | +size=1 | ||
| 341 | +stride=1 | ||
| 342 | +pad=1 | ||
| 343 | +activation=leaky | ||
| 344 | + | ||
| 345 | +[convolutional] | ||
| 346 | +batch_normalize=1 | ||
| 347 | +filters=512 | ||
| 348 | +size=3 | ||
| 349 | +stride=1 | ||
| 350 | +pad=1 | ||
| 351 | +activation=leaky | ||
| 352 | + | ||
| 353 | +[shortcut] | ||
| 354 | +from=-3 | ||
| 355 | +activation=linear | ||
| 356 | + | ||
| 357 | + | ||
| 358 | +[convolutional] | ||
| 359 | +batch_normalize=1 | ||
| 360 | +filters=256 | ||
| 361 | +size=1 | ||
| 362 | +stride=1 | ||
| 363 | +pad=1 | ||
| 364 | +activation=leaky | ||
| 365 | + | ||
| 366 | +[convolutional] | ||
| 367 | +batch_normalize=1 | ||
| 368 | +filters=512 | ||
| 369 | +size=3 | ||
| 370 | +stride=1 | ||
| 371 | +pad=1 | ||
| 372 | +activation=leaky | ||
| 373 | + | ||
| 374 | +[shortcut] | ||
| 375 | +from=-3 | ||
| 376 | +activation=linear | ||
| 377 | + | ||
| 378 | +[convolutional] | ||
| 379 | +batch_normalize=1 | ||
| 380 | +filters=256 | ||
| 381 | +size=1 | ||
| 382 | +stride=1 | ||
| 383 | +pad=1 | ||
| 384 | +activation=leaky | ||
| 385 | + | ||
| 386 | +[convolutional] | ||
| 387 | +batch_normalize=1 | ||
| 388 | +filters=512 | ||
| 389 | +size=3 | ||
| 390 | +stride=1 | ||
| 391 | +pad=1 | ||
| 392 | +activation=leaky | ||
| 393 | + | ||
| 394 | +[shortcut] | ||
| 395 | +from=-3 | ||
| 396 | +activation=linear | ||
| 397 | + | ||
| 398 | + | ||
| 399 | +[convolutional] | ||
| 400 | +batch_normalize=1 | ||
| 401 | +filters=256 | ||
| 402 | +size=1 | ||
| 403 | +stride=1 | ||
| 404 | +pad=1 | ||
| 405 | +activation=leaky | ||
| 406 | + | ||
| 407 | +[convolutional] | ||
| 408 | +batch_normalize=1 | ||
| 409 | +filters=512 | ||
| 410 | +size=3 | ||
| 411 | +stride=1 | ||
| 412 | +pad=1 | ||
| 413 | +activation=leaky | ||
| 414 | + | ||
| 415 | +[shortcut] | ||
| 416 | +from=-3 | ||
| 417 | +activation=linear | ||
| 418 | + | ||
| 419 | + | ||
| 420 | +[convolutional] | ||
| 421 | +batch_normalize=1 | ||
| 422 | +filters=256 | ||
| 423 | +size=1 | ||
| 424 | +stride=1 | ||
| 425 | +pad=1 | ||
| 426 | +activation=leaky | ||
| 427 | + | ||
| 428 | +[convolutional] | ||
| 429 | +batch_normalize=1 | ||
| 430 | +filters=512 | ||
| 431 | +size=3 | ||
| 432 | +stride=1 | ||
| 433 | +pad=1 | ||
| 434 | +activation=leaky | ||
| 435 | + | ||
| 436 | +[shortcut] | ||
| 437 | +from=-3 | ||
| 438 | +activation=linear | ||
| 439 | + | ||
| 440 | +[convolutional] | ||
| 441 | +batch_normalize=1 | ||
| 442 | +filters=256 | ||
| 443 | +size=1 | ||
| 444 | +stride=1 | ||
| 445 | +pad=1 | ||
| 446 | +activation=leaky | ||
| 447 | + | ||
| 448 | +[convolutional] | ||
| 449 | +batch_normalize=1 | ||
| 450 | +filters=512 | ||
| 451 | +size=3 | ||
| 452 | +stride=1 | ||
| 453 | +pad=1 | ||
| 454 | +activation=leaky | ||
| 455 | + | ||
| 456 | +[shortcut] | ||
| 457 | +from=-3 | ||
| 458 | +activation=linear | ||
| 459 | + | ||
| 460 | +# Downsample | ||
| 461 | + | ||
| 462 | +[convolutional] | ||
| 463 | +batch_normalize=1 | ||
| 464 | +filters=1024 | ||
| 465 | +size=3 | ||
| 466 | +stride=2 | ||
| 467 | +pad=1 | ||
| 468 | +activation=leaky | ||
| 469 | + | ||
| 470 | +[convolutional] | ||
| 471 | +batch_normalize=1 | ||
| 472 | +filters=512 | ||
| 473 | +size=1 | ||
| 474 | +stride=1 | ||
| 475 | +pad=1 | ||
| 476 | +activation=leaky | ||
| 477 | + | ||
| 478 | +[convolutional] | ||
| 479 | +batch_normalize=1 | ||
| 480 | +filters=1024 | ||
| 481 | +size=3 | ||
| 482 | +stride=1 | ||
| 483 | +pad=1 | ||
| 484 | +activation=leaky | ||
| 485 | + | ||
| 486 | +[shortcut] | ||
| 487 | +from=-3 | ||
| 488 | +activation=linear | ||
| 489 | + | ||
| 490 | +[convolutional] | ||
| 491 | +batch_normalize=1 | ||
| 492 | +filters=512 | ||
| 493 | +size=1 | ||
| 494 | +stride=1 | ||
| 495 | +pad=1 | ||
| 496 | +activation=leaky | ||
| 497 | + | ||
| 498 | +[convolutional] | ||
| 499 | +batch_normalize=1 | ||
| 500 | +filters=1024 | ||
| 501 | +size=3 | ||
| 502 | +stride=1 | ||
| 503 | +pad=1 | ||
| 504 | +activation=leaky | ||
| 505 | + | ||
| 506 | +[shortcut] | ||
| 507 | +from=-3 | ||
| 508 | +activation=linear | ||
| 509 | + | ||
| 510 | +[convolutional] | ||
| 511 | +batch_normalize=1 | ||
| 512 | +filters=512 | ||
| 513 | +size=1 | ||
| 514 | +stride=1 | ||
| 515 | +pad=1 | ||
| 516 | +activation=leaky | ||
| 517 | + | ||
| 518 | +[convolutional] | ||
| 519 | +batch_normalize=1 | ||
| 520 | +filters=1024 | ||
| 521 | +size=3 | ||
| 522 | +stride=1 | ||
| 523 | +pad=1 | ||
| 524 | +activation=leaky | ||
| 525 | + | ||
| 526 | +[shortcut] | ||
| 527 | +from=-3 | ||
| 528 | +activation=linear | ||
| 529 | + | ||
| 530 | +[convolutional] | ||
| 531 | +batch_normalize=1 | ||
| 532 | +filters=512 | ||
| 533 | +size=1 | ||
| 534 | +stride=1 | ||
| 535 | +pad=1 | ||
| 536 | +activation=leaky | ||
| 537 | + | ||
| 538 | +[convolutional] | ||
| 539 | +batch_normalize=1 | ||
| 540 | +filters=1024 | ||
| 541 | +size=3 | ||
| 542 | +stride=1 | ||
| 543 | +pad=1 | ||
| 544 | +activation=leaky | ||
| 545 | + | ||
| 546 | +[shortcut] | ||
| 547 | +from=-3 | ||
| 548 | +activation=linear | ||
| 549 | + | ||
| 550 | +###################### | ||
| 551 | + | ||
| 552 | +[convolutional] | ||
| 553 | +batch_normalize=1 | ||
| 554 | +filters=512 | ||
| 555 | +size=1 | ||
| 556 | +stride=1 | ||
| 557 | +pad=1 | ||
| 558 | +activation=leaky | ||
| 559 | + | ||
| 560 | +[convolutional] | ||
| 561 | +batch_normalize=1 | ||
| 562 | +size=3 | ||
| 563 | +stride=1 | ||
| 564 | +pad=1 | ||
| 565 | +filters=1024 | ||
| 566 | +activation=leaky | ||
| 567 | + | ||
| 568 | +[convolutional] | ||
| 569 | +batch_normalize=1 | ||
| 570 | +filters=512 | ||
| 571 | +size=1 | ||
| 572 | +stride=1 | ||
| 573 | +pad=1 | ||
| 574 | +activation=leaky | ||
| 575 | + | ||
| 576 | +[convolutional] | ||
| 577 | +batch_normalize=1 | ||
| 578 | +size=3 | ||
| 579 | +stride=1 | ||
| 580 | +pad=1 | ||
| 581 | +filters=1024 | ||
| 582 | +activation=leaky | ||
| 583 | + | ||
| 584 | +[convolutional] | ||
| 585 | +batch_normalize=1 | ||
| 586 | +filters=512 | ||
| 587 | +size=1 | ||
| 588 | +stride=1 | ||
| 589 | +pad=1 | ||
| 590 | +activation=leaky | ||
| 591 | + | ||
| 592 | +[convolutional] | ||
| 593 | +batch_normalize=1 | ||
| 594 | +size=3 | ||
| 595 | +stride=1 | ||
| 596 | +pad=1 | ||
| 597 | +filters=1024 | ||
| 598 | +activation=leaky | ||
| 599 | + | ||
| 600 | +[convolutional] | ||
| 601 | +size=1 | ||
| 602 | +stride=1 | ||
| 603 | +pad=1 | ||
| 604 | +filters=57 | ||
| 605 | +activation=linear | ||
| 606 | + | ||
| 607 | + | ||
| 608 | +[Gaussian_yolo] | ||
| 609 | +mask = 6,7,8 | ||
| 610 | +anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291 | ||
| 611 | +classes=10 | ||
| 612 | +num=9 | ||
| 613 | +jitter=.3 | ||
| 614 | +ignore_thresh = .5 | ||
| 615 | +truth_thresh = 1 | ||
| 616 | +iou_thresh=0.213 | ||
| 617 | +uc_normalizer=1.0 | ||
| 618 | +cls_normalizer=1.0 | ||
| 619 | +iou_normalizer=0.5 | ||
| 620 | +iou_loss=giou | ||
| 621 | +scale_x_y=1.0 | ||
| 622 | +random=1 | ||
| 623 | + | ||
| 624 | + | ||
| 625 | +[route] | ||
| 626 | +layers = -4 | ||
| 627 | + | ||
| 628 | +[convolutional] | ||
| 629 | +batch_normalize=1 | ||
| 630 | +filters=256 | ||
| 631 | +size=1 | ||
| 632 | +stride=1 | ||
| 633 | +pad=1 | ||
| 634 | +activation=leaky | ||
| 635 | + | ||
| 636 | +[upsample] | ||
| 637 | +stride=2 | ||
| 638 | + | ||
| 639 | +[route] | ||
| 640 | +layers = -1, 61 | ||
| 641 | + | ||
| 642 | + | ||
| 643 | + | ||
| 644 | +[convolutional] | ||
| 645 | +batch_normalize=1 | ||
| 646 | +filters=256 | ||
| 647 | +size=1 | ||
| 648 | +stride=1 | ||
| 649 | +pad=1 | ||
| 650 | +activation=leaky | ||
| 651 | + | ||
| 652 | +[convolutional] | ||
| 653 | +batch_normalize=1 | ||
| 654 | +size=3 | ||
| 655 | +stride=1 | ||
| 656 | +pad=1 | ||
| 657 | +filters=512 | ||
| 658 | +activation=leaky | ||
| 659 | + | ||
| 660 | +[convolutional] | ||
| 661 | +batch_normalize=1 | ||
| 662 | +filters=256 | ||
| 663 | +size=1 | ||
| 664 | +stride=1 | ||
| 665 | +pad=1 | ||
| 666 | +activation=leaky | ||
| 667 | + | ||
| 668 | +[convolutional] | ||
| 669 | +batch_normalize=1 | ||
| 670 | +size=3 | ||
| 671 | +stride=1 | ||
| 672 | +pad=1 | ||
| 673 | +filters=512 | ||
| 674 | +activation=leaky | ||
| 675 | + | ||
| 676 | +[convolutional] | ||
| 677 | +batch_normalize=1 | ||
| 678 | +filters=256 | ||
| 679 | +size=1 | ||
| 680 | +stride=1 | ||
| 681 | +pad=1 | ||
| 682 | +activation=leaky | ||
| 683 | + | ||
| 684 | +[convolutional] | ||
| 685 | +batch_normalize=1 | ||
| 686 | +size=3 | ||
| 687 | +stride=1 | ||
| 688 | +pad=1 | ||
| 689 | +filters=512 | ||
| 690 | +activation=leaky | ||
| 691 | + | ||
| 692 | +[convolutional] | ||
| 693 | +size=1 | ||
| 694 | +stride=1 | ||
| 695 | +pad=1 | ||
| 696 | +filters=57 | ||
| 697 | +activation=linear | ||
| 698 | + | ||
| 699 | + | ||
| 700 | +[Gaussian_yolo] | ||
| 701 | +mask = 3,4,5 | ||
| 702 | +anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291 | ||
| 703 | +classes=10 | ||
| 704 | +num=9 | ||
| 705 | +jitter=.3 | ||
| 706 | +ignore_thresh = .5 | ||
| 707 | +truth_thresh = 1 | ||
| 708 | +iou_thresh=0.213 | ||
| 709 | +uc_normalizer=1.0 | ||
| 710 | +cls_normalizer=1.0 | ||
| 711 | +iou_normalizer=0.5 | ||
| 712 | +iou_loss=giou | ||
| 713 | +scale_x_y=1.0 | ||
| 714 | +random=1 | ||
| 715 | + | ||
| 716 | + | ||
| 717 | + | ||
| 718 | +[route] | ||
| 719 | +layers = -4 | ||
| 720 | + | ||
| 721 | +[convolutional] | ||
| 722 | +batch_normalize=1 | ||
| 723 | +filters=128 | ||
| 724 | +size=1 | ||
| 725 | +stride=1 | ||
| 726 | +pad=1 | ||
| 727 | +activation=leaky | ||
| 728 | + | ||
| 729 | +[upsample] | ||
| 730 | +stride=2 | ||
| 731 | + | ||
| 732 | +[route] | ||
| 733 | +layers = -1, 36 | ||
| 734 | + | ||
| 735 | + | ||
| 736 | + | ||
| 737 | +[convolutional] | ||
| 738 | +batch_normalize=1 | ||
| 739 | +filters=128 | ||
| 740 | +size=1 | ||
| 741 | +stride=1 | ||
| 742 | +pad=1 | ||
| 743 | +activation=leaky | ||
| 744 | + | ||
| 745 | +[convolutional] | ||
| 746 | +batch_normalize=1 | ||
| 747 | +size=3 | ||
| 748 | +stride=1 | ||
| 749 | +pad=1 | ||
| 750 | +filters=256 | ||
| 751 | +activation=leaky | ||
| 752 | + | ||
| 753 | +[convolutional] | ||
| 754 | +batch_normalize=1 | ||
| 755 | +filters=128 | ||
| 756 | +size=1 | ||
| 757 | +stride=1 | ||
| 758 | +pad=1 | ||
| 759 | +activation=leaky | ||
| 760 | + | ||
| 761 | +[convolutional] | ||
| 762 | +batch_normalize=1 | ||
| 763 | +size=3 | ||
| 764 | +stride=1 | ||
| 765 | +pad=1 | ||
| 766 | +filters=256 | ||
| 767 | +activation=leaky | ||
| 768 | + | ||
| 769 | +[convolutional] | ||
| 770 | +batch_normalize=1 | ||
| 771 | +filters=128 | ||
| 772 | +size=1 | ||
| 773 | +stride=1 | ||
| 774 | +pad=1 | ||
| 775 | +activation=leaky | ||
| 776 | + | ||
| 777 | +[convolutional] | ||
| 778 | +batch_normalize=1 | ||
| 779 | +size=3 | ||
| 780 | +stride=1 | ||
| 781 | +pad=1 | ||
| 782 | +filters=256 | ||
| 783 | +activation=leaky | ||
| 784 | + | ||
| 785 | +[convolutional] | ||
| 786 | +size=1 | ||
| 787 | +stride=1 | ||
| 788 | +pad=1 | ||
| 789 | +filters=57 | ||
| 790 | +activation=linear | ||
| 791 | + | ||
| 792 | + | ||
| 793 | +[Gaussian_yolo] | ||
| 794 | +mask = 0,1,2 | ||
| 795 | +anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291 | ||
| 796 | +classes=10 | ||
| 797 | +num=9 | ||
| 798 | +jitter=.3 | ||
| 799 | +ignore_thresh = .5 | ||
| 800 | +truth_thresh = 1 | ||
| 801 | +iou_thresh=0.213 | ||
| 802 | +uc_normalizer=1.0 | ||
| 803 | +cls_normalizer=1.0 | ||
| 804 | +iou_normalizer=0.5 | ||
| 805 | +iou_loss=giou | ||
| 806 | +scale_x_y=1.0 | ||
| 807 | +random=1 |
| 1 | +[net] | ||
| 2 | +batch=128 | ||
| 3 | +subdivisions=1 | ||
| 4 | +height=227 | ||
| 5 | +width=227 | ||
| 6 | +channels=3 | ||
| 7 | +momentum=0.9 | ||
| 8 | +decay=0.0005 | ||
| 9 | +max_crop=256 | ||
| 10 | + | ||
| 11 | +learning_rate=0.01 | ||
| 12 | +policy=poly | ||
| 13 | +power=4 | ||
| 14 | +max_batches=800000 | ||
| 15 | + | ||
| 16 | +angle=7 | ||
| 17 | +hue = .1 | ||
| 18 | +saturation=.75 | ||
| 19 | +exposure=.75 | ||
| 20 | +aspect=.75 | ||
| 21 | + | ||
| 22 | +[convolutional] | ||
| 23 | +filters=96 | ||
| 24 | +size=11 | ||
| 25 | +stride=4 | ||
| 26 | +pad=0 | ||
| 27 | +activation=relu | ||
| 28 | + | ||
| 29 | +[maxpool] | ||
| 30 | +size=3 | ||
| 31 | +stride=2 | ||
| 32 | +padding=0 | ||
| 33 | + | ||
| 34 | +[convolutional] | ||
| 35 | +filters=256 | ||
| 36 | +size=5 | ||
| 37 | +stride=1 | ||
| 38 | +pad=1 | ||
| 39 | +activation=relu | ||
| 40 | + | ||
| 41 | +[maxpool] | ||
| 42 | +size=3 | ||
| 43 | +stride=2 | ||
| 44 | +padding=0 | ||
| 45 | + | ||
| 46 | +[convolutional] | ||
| 47 | +filters=384 | ||
| 48 | +size=3 | ||
| 49 | +stride=1 | ||
| 50 | +pad=1 | ||
| 51 | +activation=relu | ||
| 52 | + | ||
| 53 | +[convolutional] | ||
| 54 | +filters=384 | ||
| 55 | +size=3 | ||
| 56 | +stride=1 | ||
| 57 | +pad=1 | ||
| 58 | +activation=relu | ||
| 59 | + | ||
| 60 | +[convolutional] | ||
| 61 | +filters=256 | ||
| 62 | +size=3 | ||
| 63 | +stride=1 | ||
| 64 | +pad=1 | ||
| 65 | +activation=relu | ||
| 66 | + | ||
| 67 | +[maxpool] | ||
| 68 | +size=3 | ||
| 69 | +stride=2 | ||
| 70 | +padding=0 | ||
| 71 | + | ||
| 72 | +[connected] | ||
| 73 | +output=4096 | ||
| 74 | +activation=relu | ||
| 75 | + | ||
| 76 | +[dropout] | ||
| 77 | +probability=.5 | ||
| 78 | + | ||
| 79 | +[connected] | ||
| 80 | +output=4096 | ||
| 81 | +activation=relu | ||
| 82 | + | ||
| 83 | +[dropout] | ||
| 84 | +probability=.5 | ||
| 85 | + | ||
| 86 | +[connected] | ||
| 87 | +output=1000 | ||
| 88 | +activation=linear | ||
| 89 | + | ||
| 90 | +[softmax] | ||
| 91 | +groups=1 | ||
| 92 | + | ||
| 93 | +[cost] | ||
| 94 | +type=sse | ||
| 95 | + |
| 1 | +[net] | ||
| 2 | +batch=128 | ||
| 3 | +subdivisions=1 | ||
| 4 | +height=32 | ||
| 5 | +width=32 | ||
| 6 | +channels=3 | ||
| 7 | +momentum=0.9 | ||
| 8 | +decay=0.0005 | ||
| 9 | + | ||
| 10 | +learning_rate=0.4 | ||
| 11 | +policy=poly | ||
| 12 | +power=4 | ||
| 13 | +max_batches = 50000 | ||
| 14 | + | ||
| 15 | +[crop] | ||
| 16 | +crop_width=28 | ||
| 17 | +crop_height=28 | ||
| 18 | +flip=1 | ||
| 19 | +angle=0 | ||
| 20 | +saturation = 1 | ||
| 21 | +exposure = 1 | ||
| 22 | +noadjust=1 | ||
| 23 | + | ||
| 24 | +[convolutional] | ||
| 25 | +batch_normalize=1 | ||
| 26 | +filters=128 | ||
| 27 | +size=3 | ||
| 28 | +stride=1 | ||
| 29 | +pad=1 | ||
| 30 | +activation=leaky | ||
| 31 | + | ||
| 32 | +[convolutional] | ||
| 33 | +batch_normalize=1 | ||
| 34 | +filters=128 | ||
| 35 | +size=3 | ||
| 36 | +stride=1 | ||
| 37 | +pad=1 | ||
| 38 | +activation=leaky | ||
| 39 | + | ||
| 40 | +[convolutional] | ||
| 41 | +batch_normalize=1 | ||
| 42 | +filters=128 | ||
| 43 | +size=3 | ||
| 44 | +stride=1 | ||
| 45 | +pad=1 | ||
| 46 | +activation=leaky | ||
| 47 | + | ||
| 48 | +[maxpool] | ||
| 49 | +size=2 | ||
| 50 | +stride=2 | ||
| 51 | + | ||
| 52 | +[dropout] | ||
| 53 | +probability=.5 | ||
| 54 | + | ||
| 55 | +[convolutional] | ||
| 56 | +batch_normalize=1 | ||
| 57 | +filters=256 | ||
| 58 | +size=3 | ||
| 59 | +stride=1 | ||
| 60 | +pad=1 | ||
| 61 | +activation=leaky | ||
| 62 | + | ||
| 63 | +[convolutional] | ||
| 64 | +batch_normalize=1 | ||
| 65 | +filters=256 | ||
| 66 | +size=3 | ||
| 67 | +stride=1 | ||
| 68 | +pad=1 | ||
| 69 | +activation=leaky | ||
| 70 | + | ||
| 71 | +[convolutional] | ||
| 72 | +batch_normalize=1 | ||
| 73 | +filters=256 | ||
| 74 | +size=3 | ||
| 75 | +stride=1 | ||
| 76 | +pad=1 | ||
| 77 | +activation=leaky | ||
| 78 | + | ||
| 79 | +[maxpool] | ||
| 80 | +size=2 | ||
| 81 | +stride=2 | ||
| 82 | + | ||
| 83 | +[dropout] | ||
| 84 | +probability=.5 | ||
| 85 | + | ||
| 86 | +[convolutional] | ||
| 87 | +batch_normalize=1 | ||
| 88 | +filters=512 | ||
| 89 | +size=3 | ||
| 90 | +stride=1 | ||
| 91 | +pad=1 | ||
| 92 | +activation=leaky | ||
| 93 | + | ||
| 94 | +[convolutional] | ||
| 95 | +batch_normalize=1 | ||
| 96 | +filters=512 | ||
| 97 | +size=3 | ||
| 98 | +stride=1 | ||
| 99 | +pad=1 | ||
| 100 | +activation=leaky | ||
| 101 | + | ||
| 102 | +[convolutional] | ||
| 103 | +batch_normalize=1 | ||
| 104 | +filters=512 | ||
| 105 | +size=3 | ||
| 106 | +stride=1 | ||
| 107 | +pad=1 | ||
| 108 | +activation=leaky | ||
| 109 | + | ||
| 110 | +[dropout] | ||
| 111 | +probability=.5 | ||
| 112 | + | ||
| 113 | +[convolutional] | ||
| 114 | +filters=10 | ||
| 115 | +size=1 | ||
| 116 | +stride=1 | ||
| 117 | +pad=1 | ||
| 118 | +activation=leaky | ||
| 119 | + | ||
| 120 | +[avgpool] | ||
| 121 | + | ||
| 122 | +[softmax] | ||
| 123 | +groups=1 | ||
| 124 | + | ||
| 125 | +[cost] | ||
| 126 | + |
| 1 | +[net] | ||
| 2 | +batch=128 | ||
| 3 | +subdivisions=1 | ||
| 4 | +height=32 | ||
| 5 | +width=32 | ||
| 6 | +channels=3 | ||
| 7 | +momentum=0.9 | ||
| 8 | +decay=0.0005 | ||
| 9 | + | ||
| 10 | +learning_rate=0.4 | ||
| 11 | +policy=poly | ||
| 12 | +power=4 | ||
| 13 | +max_batches = 50000 | ||
| 14 | + | ||
| 15 | + | ||
| 16 | +[convolutional] | ||
| 17 | +batch_normalize=1 | ||
| 18 | +filters=128 | ||
| 19 | +size=3 | ||
| 20 | +stride=1 | ||
| 21 | +pad=1 | ||
| 22 | +activation=leaky | ||
| 23 | + | ||
| 24 | +[convolutional] | ||
| 25 | +batch_normalize=1 | ||
| 26 | +filters=128 | ||
| 27 | +size=3 | ||
| 28 | +stride=1 | ||
| 29 | +pad=1 | ||
| 30 | +activation=leaky | ||
| 31 | + | ||
| 32 | +[convolutional] | ||
| 33 | +batch_normalize=1 | ||
| 34 | +filters=128 | ||
| 35 | +size=3 | ||
| 36 | +stride=1 | ||
| 37 | +pad=1 | ||
| 38 | +activation=leaky | ||
| 39 | + | ||
| 40 | +[maxpool] | ||
| 41 | +size=2 | ||
| 42 | +stride=2 | ||
| 43 | + | ||
| 44 | +[dropout] | ||
| 45 | +probability=.5 | ||
| 46 | + | ||
| 47 | +[convolutional] | ||
| 48 | +batch_normalize=1 | ||
| 49 | +filters=256 | ||
| 50 | +size=3 | ||
| 51 | +stride=1 | ||
| 52 | +pad=1 | ||
| 53 | +activation=leaky | ||
| 54 | + | ||
| 55 | +[convolutional] | ||
| 56 | +batch_normalize=1 | ||
| 57 | +filters=256 | ||
| 58 | +size=3 | ||
| 59 | +stride=1 | ||
| 60 | +pad=1 | ||
| 61 | +activation=leaky | ||
| 62 | + | ||
| 63 | +[convolutional] | ||
| 64 | +batch_normalize=1 | ||
| 65 | +filters=256 | ||
| 66 | +size=3 | ||
| 67 | +stride=1 | ||
| 68 | +pad=1 | ||
| 69 | +activation=leaky | ||
| 70 | + | ||
| 71 | +[maxpool] | ||
| 72 | +size=2 | ||
| 73 | +stride=2 | ||
| 74 | + | ||
| 75 | +[dropout] | ||
| 76 | +probability=.5 | ||
| 77 | + | ||
| 78 | +[convolutional] | ||
| 79 | +batch_normalize=1 | ||
| 80 | +filters=512 | ||
| 81 | +size=3 | ||
| 82 | +stride=1 | ||
| 83 | +pad=1 | ||
| 84 | +activation=leaky | ||
| 85 | + | ||
| 86 | +[convolutional] | ||
| 87 | +batch_normalize=1 | ||
| 88 | +filters=512 | ||
| 89 | +size=3 | ||
| 90 | +stride=1 | ||
| 91 | +pad=1 | ||
| 92 | +activation=leaky | ||
| 93 | + | ||
| 94 | +[convolutional] | ||
| 95 | +batch_normalize=1 | ||
| 96 | +filters=512 | ||
| 97 | +size=3 | ||
| 98 | +stride=1 | ||
| 99 | +pad=1 | ||
| 100 | +activation=leaky | ||
| 101 | + | ||
| 102 | +[dropout] | ||
| 103 | +probability=.5 | ||
| 104 | + | ||
| 105 | +[convolutional] | ||
| 106 | +filters=10 | ||
| 107 | +size=1 | ||
| 108 | +stride=1 | ||
| 109 | +pad=1 | ||
| 110 | +activation=leaky | ||
| 111 | + | ||
| 112 | +[avgpool] | ||
| 113 | + | ||
| 114 | +[softmax] | ||
| 115 | +groups=1 | ||
| 116 | +temperature=3 | ||
| 117 | + | ||
| 118 | +[cost] | ||
| 119 | + |
| 1 | +[net] | ||
| 2 | +subdivisions=8 | ||
| 3 | +inputs=256 | ||
| 4 | +batch = 128 | ||
| 5 | +momentum=0.9 | ||
| 6 | +decay=0.001 | ||
| 7 | +max_batches = 2000 | ||
| 8 | +time_steps=576 | ||
| 9 | +learning_rate=0.1 | ||
| 10 | +policy=steps | ||
| 11 | +steps=1000,1500 | ||
| 12 | +scales=.1,.1 | ||
| 13 | + | ||
| 14 | +try_fix_nan=1 | ||
| 15 | + | ||
| 16 | +[connected] | ||
| 17 | +output=256 | ||
| 18 | +activation=leaky | ||
| 19 | + | ||
| 20 | +[crnn] | ||
| 21 | +batch_normalize=1 | ||
| 22 | +size=1 | ||
| 23 | +pad=0 | ||
| 24 | +output = 1024 | ||
| 25 | +hidden=1024 | ||
| 26 | +activation=leaky | ||
| 27 | + | ||
| 28 | +[crnn] | ||
| 29 | +batch_normalize=1 | ||
| 30 | +size=1 | ||
| 31 | +pad=0 | ||
| 32 | +output = 1024 | ||
| 33 | +hidden=1024 | ||
| 34 | +activation=leaky | ||
| 35 | + | ||
| 36 | +[crnn] | ||
| 37 | +batch_normalize=1 | ||
| 38 | +size=1 | ||
| 39 | +pad=0 | ||
| 40 | +output = 1024 | ||
| 41 | +hidden=1024 | ||
| 42 | +activation=leaky | ||
| 43 | + | ||
| 44 | +[connected] | ||
| 45 | +output=256 | ||
| 46 | +activation=leaky | ||
| 47 | + | ||
| 48 | +[softmax] | ||
| 49 | + | ||
| 50 | +[cost] | ||
| 51 | +type=sse | ||
| 52 | + |
| 1 | +[net] | ||
| 2 | +# Training | ||
| 3 | +batch=128 | ||
| 4 | +subdivisions=4 | ||
| 5 | + | ||
| 6 | +label_smooth_eps=0.1 | ||
| 7 | + | ||
| 8 | +# Testing | ||
| 9 | +# batch=1 | ||
| 10 | +# subdivisions=1 | ||
| 11 | + | ||
| 12 | +height=256 | ||
| 13 | +width=256 | ||
| 14 | +channels=3 | ||
| 15 | +min_crop=128 | ||
| 16 | +max_crop=448 | ||
| 17 | + | ||
| 18 | +mosaic=1 | ||
| 19 | +cutmix=1 | ||
| 20 | + | ||
| 21 | +burn_in=1000 | ||
| 22 | +learning_rate=0.1 | ||
| 23 | +policy=poly | ||
| 24 | +power=4 | ||
| 25 | +max_batches=1200000 | ||
| 26 | +momentum=0.9 | ||
| 27 | +decay=0.0005 | ||
| 28 | + | ||
| 29 | +angle=7 | ||
| 30 | +hue=.1 | ||
| 31 | +saturation=.75 | ||
| 32 | +exposure=.75 | ||
| 33 | +aspect=.75 | ||
| 34 | + | ||
| 35 | + | ||
| 36 | + | ||
| 37 | +[convolutional] | ||
| 38 | +batch_normalize=1 | ||
| 39 | +filters=32 | ||
| 40 | +size=3 | ||
| 41 | +stride=1 | ||
| 42 | +pad=1 | ||
| 43 | +activation=mish | ||
| 44 | + | ||
| 45 | +# Downsample | ||
| 46 | + | ||
| 47 | +[convolutional] | ||
| 48 | +batch_normalize=1 | ||
| 49 | +filters=64 | ||
| 50 | +size=3 | ||
| 51 | +stride=2 | ||
| 52 | +pad=1 | ||
| 53 | +activation=mish | ||
| 54 | + | ||
| 55 | +[convolutional] | ||
| 56 | +batch_normalize=1 | ||
| 57 | +filters=64 | ||
| 58 | +size=1 | ||
| 59 | +stride=1 | ||
| 60 | +pad=1 | ||
| 61 | +activation=mish | ||
| 62 | + | ||
| 63 | +[route] | ||
| 64 | +layers = -2 | ||
| 65 | + | ||
| 66 | +[convolutional] | ||
| 67 | +batch_normalize=1 | ||
| 68 | +filters=64 | ||
| 69 | +size=1 | ||
| 70 | +stride=1 | ||
| 71 | +pad=1 | ||
| 72 | +activation=mish | ||
| 73 | + | ||
| 74 | +[convolutional] | ||
| 75 | +batch_normalize=1 | ||
| 76 | +filters=32 | ||
| 77 | +size=1 | ||
| 78 | +stride=1 | ||
| 79 | +pad=1 | ||
| 80 | +activation=mish | ||
| 81 | + | ||
| 82 | +[convolutional] | ||
| 83 | +batch_normalize=1 | ||
| 84 | +filters=64 | ||
| 85 | +size=3 | ||
| 86 | +stride=1 | ||
| 87 | +pad=1 | ||
| 88 | +activation=mish | ||
| 89 | + | ||
| 90 | +[shortcut] | ||
| 91 | +from=-3 | ||
| 92 | +activation=linear | ||
| 93 | + | ||
| 94 | +[convolutional] | ||
| 95 | +batch_normalize=1 | ||
| 96 | +filters=64 | ||
| 97 | +size=1 | ||
| 98 | +stride=1 | ||
| 99 | +pad=1 | ||
| 100 | +activation=mish | ||
| 101 | + | ||
| 102 | +[route] | ||
| 103 | +layers = -1,-7 | ||
| 104 | + | ||
| 105 | +[convolutional] | ||
| 106 | +batch_normalize=1 | ||
| 107 | +filters=64 | ||
| 108 | +size=1 | ||
| 109 | +stride=1 | ||
| 110 | +pad=1 | ||
| 111 | +activation=mish | ||
| 112 | + | ||
| 113 | +# Downsample | ||
| 114 | + | ||
| 115 | +[convolutional] | ||
| 116 | +batch_normalize=1 | ||
| 117 | +filters=128 | ||
| 118 | +size=3 | ||
| 119 | +stride=2 | ||
| 120 | +pad=1 | ||
| 121 | +activation=mish | ||
| 122 | + | ||
| 123 | +[convolutional] | ||
| 124 | +batch_normalize=1 | ||
| 125 | +filters=64 | ||
| 126 | +size=1 | ||
| 127 | +stride=1 | ||
| 128 | +pad=1 | ||
| 129 | +activation=mish | ||
| 130 | + | ||
| 131 | +[route] | ||
| 132 | +layers = -2 | ||
| 133 | + | ||
| 134 | +[convolutional] | ||
| 135 | +batch_normalize=1 | ||
| 136 | +filters=64 | ||
| 137 | +size=1 | ||
| 138 | +stride=1 | ||
| 139 | +pad=1 | ||
| 140 | +activation=mish | ||
| 141 | + | ||
| 142 | +[convolutional] | ||
| 143 | +batch_normalize=1 | ||
| 144 | +filters=64 | ||
| 145 | +size=1 | ||
| 146 | +stride=1 | ||
| 147 | +pad=1 | ||
| 148 | +activation=mish | ||
| 149 | + | ||
| 150 | +[convolutional] | ||
| 151 | +batch_normalize=1 | ||
| 152 | +filters=64 | ||
| 153 | +size=3 | ||
| 154 | +stride=1 | ||
| 155 | +pad=1 | ||
| 156 | +activation=mish | ||
| 157 | + | ||
| 158 | +[shortcut] | ||
| 159 | +from=-3 | ||
| 160 | +activation=linear | ||
| 161 | + | ||
| 162 | +[convolutional] | ||
| 163 | +batch_normalize=1 | ||
| 164 | +filters=64 | ||
| 165 | +size=1 | ||
| 166 | +stride=1 | ||
| 167 | +pad=1 | ||
| 168 | +activation=mish | ||
| 169 | + | ||
| 170 | +[convolutional] | ||
| 171 | +batch_normalize=1 | ||
| 172 | +filters=64 | ||
| 173 | +size=3 | ||
| 174 | +stride=1 | ||
| 175 | +pad=1 | ||
| 176 | +activation=mish | ||
| 177 | + | ||
| 178 | +[shortcut] | ||
| 179 | +from=-3 | ||
| 180 | +activation=linear | ||
| 181 | + | ||
| 182 | +[convolutional] | ||
| 183 | +batch_normalize=1 | ||
| 184 | +filters=64 | ||
| 185 | +size=1 | ||
| 186 | +stride=1 | ||
| 187 | +pad=1 | ||
| 188 | +activation=mish | ||
| 189 | + | ||
| 190 | +[route] | ||
| 191 | +layers = -1,-10 | ||
| 192 | + | ||
| 193 | +[convolutional] | ||
| 194 | +batch_normalize=1 | ||
| 195 | +filters=128 | ||
| 196 | +size=1 | ||
| 197 | +stride=1 | ||
| 198 | +pad=1 | ||
| 199 | +activation=mish | ||
| 200 | + | ||
| 201 | +# Downsample | ||
| 202 | + | ||
| 203 | +[convolutional] | ||
| 204 | +batch_normalize=1 | ||
| 205 | +filters=256 | ||
| 206 | +size=3 | ||
| 207 | +stride=2 | ||
| 208 | +pad=1 | ||
| 209 | +activation=mish | ||
| 210 | + | ||
| 211 | +[convolutional] | ||
| 212 | +batch_normalize=1 | ||
| 213 | +filters=128 | ||
| 214 | +size=1 | ||
| 215 | +stride=1 | ||
| 216 | +pad=1 | ||
| 217 | +activation=mish | ||
| 218 | + | ||
| 219 | +[route] | ||
| 220 | +layers = -2 | ||
| 221 | + | ||
| 222 | +[convolutional] | ||
| 223 | +batch_normalize=1 | ||
| 224 | +filters=128 | ||
| 225 | +size=1 | ||
| 226 | +stride=1 | ||
| 227 | +pad=1 | ||
| 228 | +activation=mish | ||
| 229 | + | ||
| 230 | +[convolutional] | ||
| 231 | +batch_normalize=1 | ||
| 232 | +filters=128 | ||
| 233 | +size=1 | ||
| 234 | +stride=1 | ||
| 235 | +pad=1 | ||
| 236 | +activation=mish | ||
| 237 | + | ||
| 238 | +[convolutional] | ||
| 239 | +batch_normalize=1 | ||
| 240 | +filters=128 | ||
| 241 | +size=3 | ||
| 242 | +stride=1 | ||
| 243 | +pad=1 | ||
| 244 | +activation=mish | ||
| 245 | + | ||
| 246 | +[shortcut] | ||
| 247 | +from=-3 | ||
| 248 | +activation=linear | ||
| 249 | + | ||
| 250 | +[convolutional] | ||
| 251 | +batch_normalize=1 | ||
| 252 | +filters=128 | ||
| 253 | +size=1 | ||
| 254 | +stride=1 | ||
| 255 | +pad=1 | ||
| 256 | +activation=mish | ||
| 257 | + | ||
| 258 | +[convolutional] | ||
| 259 | +batch_normalize=1 | ||
| 260 | +filters=128 | ||
| 261 | +size=3 | ||
| 262 | +stride=1 | ||
| 263 | +pad=1 | ||
| 264 | +activation=mish | ||
| 265 | + | ||
| 266 | +[shortcut] | ||
| 267 | +from=-3 | ||
| 268 | +activation=linear | ||
| 269 | + | ||
| 270 | +[convolutional] | ||
| 271 | +batch_normalize=1 | ||
| 272 | +filters=128 | ||
| 273 | +size=1 | ||
| 274 | +stride=1 | ||
| 275 | +pad=1 | ||
| 276 | +activation=mish | ||
| 277 | + | ||
| 278 | +[convolutional] | ||
| 279 | +batch_normalize=1 | ||
| 280 | +filters=128 | ||
| 281 | +size=3 | ||
| 282 | +stride=1 | ||
| 283 | +pad=1 | ||
| 284 | +activation=mish | ||
| 285 | + | ||
| 286 | +[shortcut] | ||
| 287 | +from=-3 | ||
| 288 | +activation=linear | ||
| 289 | + | ||
| 290 | +[convolutional] | ||
| 291 | +batch_normalize=1 | ||
| 292 | +filters=128 | ||
| 293 | +size=1 | ||
| 294 | +stride=1 | ||
| 295 | +pad=1 | ||
| 296 | +activation=mish | ||
| 297 | + | ||
| 298 | +[convolutional] | ||
| 299 | +batch_normalize=1 | ||
| 300 | +filters=128 | ||
| 301 | +size=3 | ||
| 302 | +stride=1 | ||
| 303 | +pad=1 | ||
| 304 | +activation=mish | ||
| 305 | + | ||
| 306 | +[shortcut] | ||
| 307 | +from=-3 | ||
| 308 | +activation=linear | ||
| 309 | + | ||
| 310 | + | ||
| 311 | +[convolutional] | ||
| 312 | +batch_normalize=1 | ||
| 313 | +filters=128 | ||
| 314 | +size=1 | ||
| 315 | +stride=1 | ||
| 316 | +pad=1 | ||
| 317 | +activation=mish | ||
| 318 | + | ||
| 319 | +[convolutional] | ||
| 320 | +batch_normalize=1 | ||
| 321 | +filters=128 | ||
| 322 | +size=3 | ||
| 323 | +stride=1 | ||
| 324 | +pad=1 | ||
| 325 | +activation=mish | ||
| 326 | + | ||
| 327 | +[shortcut] | ||
| 328 | +from=-3 | ||
| 329 | +activation=linear | ||
| 330 | + | ||
| 331 | +[convolutional] | ||
| 332 | +batch_normalize=1 | ||
| 333 | +filters=128 | ||
| 334 | +size=1 | ||
| 335 | +stride=1 | ||
| 336 | +pad=1 | ||
| 337 | +activation=mish | ||
| 338 | + | ||
| 339 | +[convolutional] | ||
| 340 | +batch_normalize=1 | ||
| 341 | +filters=128 | ||
| 342 | +size=3 | ||
| 343 | +stride=1 | ||
| 344 | +pad=1 | ||
| 345 | +activation=mish | ||
| 346 | + | ||
| 347 | +[shortcut] | ||
| 348 | +from=-3 | ||
| 349 | +activation=linear | ||
| 350 | + | ||
| 351 | +[convolutional] | ||
| 352 | +batch_normalize=1 | ||
| 353 | +filters=128 | ||
| 354 | +size=1 | ||
| 355 | +stride=1 | ||
| 356 | +pad=1 | ||
| 357 | +activation=mish | ||
| 358 | + | ||
| 359 | +[convolutional] | ||
| 360 | +batch_normalize=1 | ||
| 361 | +filters=128 | ||
| 362 | +size=3 | ||
| 363 | +stride=1 | ||
| 364 | +pad=1 | ||
| 365 | +activation=mish | ||
| 366 | + | ||
| 367 | +[shortcut] | ||
| 368 | +from=-3 | ||
| 369 | +activation=linear | ||
| 370 | + | ||
| 371 | +[convolutional] | ||
| 372 | +batch_normalize=1 | ||
| 373 | +filters=128 | ||
| 374 | +size=1 | ||
| 375 | +stride=1 | ||
| 376 | +pad=1 | ||
| 377 | +activation=mish | ||
| 378 | + | ||
| 379 | +[convolutional] | ||
| 380 | +batch_normalize=1 | ||
| 381 | +filters=128 | ||
| 382 | +size=3 | ||
| 383 | +stride=1 | ||
| 384 | +pad=1 | ||
| 385 | +activation=mish | ||
| 386 | + | ||
| 387 | +[shortcut] | ||
| 388 | +from=-3 | ||
| 389 | +activation=linear | ||
| 390 | + | ||
| 391 | +[convolutional] | ||
| 392 | +batch_normalize=1 | ||
| 393 | +filters=128 | ||
| 394 | +size=1 | ||
| 395 | +stride=1 | ||
| 396 | +pad=1 | ||
| 397 | +activation=mish | ||
| 398 | + | ||
| 399 | +[route] | ||
| 400 | +layers = -1,-28 | ||
| 401 | + | ||
| 402 | +[convolutional] | ||
| 403 | +batch_normalize=1 | ||
| 404 | +filters=256 | ||
| 405 | +size=1 | ||
| 406 | +stride=1 | ||
| 407 | +pad=1 | ||
| 408 | +activation=mish | ||
| 409 | + | ||
| 410 | +# Downsample | ||
| 411 | + | ||
| 412 | +[convolutional] | ||
| 413 | +batch_normalize=1 | ||
| 414 | +filters=512 | ||
| 415 | +size=3 | ||
| 416 | +stride=2 | ||
| 417 | +pad=1 | ||
| 418 | +activation=mish | ||
| 419 | + | ||
| 420 | +[convolutional] | ||
| 421 | +batch_normalize=1 | ||
| 422 | +filters=256 | ||
| 423 | +size=1 | ||
| 424 | +stride=1 | ||
| 425 | +pad=1 | ||
| 426 | +activation=mish | ||
| 427 | + | ||
| 428 | +[route] | ||
| 429 | +layers = -2 | ||
| 430 | + | ||
| 431 | +[convolutional] | ||
| 432 | +batch_normalize=1 | ||
| 433 | +filters=256 | ||
| 434 | +size=1 | ||
| 435 | +stride=1 | ||
| 436 | +pad=1 | ||
| 437 | +activation=mish | ||
| 438 | + | ||
| 439 | +[convolutional] | ||
| 440 | +batch_normalize=1 | ||
| 441 | +filters=256 | ||
| 442 | +size=1 | ||
| 443 | +stride=1 | ||
| 444 | +pad=1 | ||
| 445 | +activation=mish | ||
| 446 | + | ||
| 447 | +[convolutional] | ||
| 448 | +batch_normalize=1 | ||
| 449 | +filters=256 | ||
| 450 | +size=3 | ||
| 451 | +stride=1 | ||
| 452 | +pad=1 | ||
| 453 | +activation=mish | ||
| 454 | + | ||
| 455 | +[shortcut] | ||
| 456 | +from=-3 | ||
| 457 | +activation=linear | ||
| 458 | + | ||
| 459 | + | ||
| 460 | +[convolutional] | ||
| 461 | +batch_normalize=1 | ||
| 462 | +filters=256 | ||
| 463 | +size=1 | ||
| 464 | +stride=1 | ||
| 465 | +pad=1 | ||
| 466 | +activation=mish | ||
| 467 | + | ||
| 468 | +[convolutional] | ||
| 469 | +batch_normalize=1 | ||
| 470 | +filters=256 | ||
| 471 | +size=3 | ||
| 472 | +stride=1 | ||
| 473 | +pad=1 | ||
| 474 | +activation=mish | ||
| 475 | + | ||
| 476 | +[shortcut] | ||
| 477 | +from=-3 | ||
| 478 | +activation=linear | ||
| 479 | + | ||
| 480 | + | ||
| 481 | +[convolutional] | ||
| 482 | +batch_normalize=1 | ||
| 483 | +filters=256 | ||
| 484 | +size=1 | ||
| 485 | +stride=1 | ||
| 486 | +pad=1 | ||
| 487 | +activation=mish | ||
| 488 | + | ||
| 489 | +[convolutional] | ||
| 490 | +batch_normalize=1 | ||
| 491 | +filters=256 | ||
| 492 | +size=3 | ||
| 493 | +stride=1 | ||
| 494 | +pad=1 | ||
| 495 | +activation=mish | ||
| 496 | + | ||
| 497 | +[shortcut] | ||
| 498 | +from=-3 | ||
| 499 | +activation=linear | ||
| 500 | + | ||
| 501 | + | ||
| 502 | +[convolutional] | ||
| 503 | +batch_normalize=1 | ||
| 504 | +filters=256 | ||
| 505 | +size=1 | ||
| 506 | +stride=1 | ||
| 507 | +pad=1 | ||
| 508 | +activation=mish | ||
| 509 | + | ||
| 510 | +[convolutional] | ||
| 511 | +batch_normalize=1 | ||
| 512 | +filters=256 | ||
| 513 | +size=3 | ||
| 514 | +stride=1 | ||
| 515 | +pad=1 | ||
| 516 | +activation=mish | ||
| 517 | + | ||
| 518 | +[shortcut] | ||
| 519 | +from=-3 | ||
| 520 | +activation=linear | ||
| 521 | + | ||
| 522 | + | ||
| 523 | +[convolutional] | ||
| 524 | +batch_normalize=1 | ||
| 525 | +filters=256 | ||
| 526 | +size=1 | ||
| 527 | +stride=1 | ||
| 528 | +pad=1 | ||
| 529 | +activation=mish | ||
| 530 | + | ||
| 531 | +[convolutional] | ||
| 532 | +batch_normalize=1 | ||
| 533 | +filters=256 | ||
| 534 | +size=3 | ||
| 535 | +stride=1 | ||
| 536 | +pad=1 | ||
| 537 | +activation=mish | ||
| 538 | + | ||
| 539 | +[shortcut] | ||
| 540 | +from=-3 | ||
| 541 | +activation=linear | ||
| 542 | + | ||
| 543 | + | ||
| 544 | +[convolutional] | ||
| 545 | +batch_normalize=1 | ||
| 546 | +filters=256 | ||
| 547 | +size=1 | ||
| 548 | +stride=1 | ||
| 549 | +pad=1 | ||
| 550 | +activation=mish | ||
| 551 | + | ||
| 552 | +[convolutional] | ||
| 553 | +batch_normalize=1 | ||
| 554 | +filters=256 | ||
| 555 | +size=3 | ||
| 556 | +stride=1 | ||
| 557 | +pad=1 | ||
| 558 | +activation=mish | ||
| 559 | + | ||
| 560 | +[shortcut] | ||
| 561 | +from=-3 | ||
| 562 | +activation=linear | ||
| 563 | + | ||
| 564 | + | ||
| 565 | +[convolutional] | ||
| 566 | +batch_normalize=1 | ||
| 567 | +filters=256 | ||
| 568 | +size=1 | ||
| 569 | +stride=1 | ||
| 570 | +pad=1 | ||
| 571 | +activation=mish | ||
| 572 | + | ||
| 573 | +[convolutional] | ||
| 574 | +batch_normalize=1 | ||
| 575 | +filters=256 | ||
| 576 | +size=3 | ||
| 577 | +stride=1 | ||
| 578 | +pad=1 | ||
| 579 | +activation=mish | ||
| 580 | + | ||
| 581 | +[shortcut] | ||
| 582 | +from=-3 | ||
| 583 | +activation=linear | ||
| 584 | + | ||
| 585 | +[convolutional] | ||
| 586 | +batch_normalize=1 | ||
| 587 | +filters=256 | ||
| 588 | +size=1 | ||
| 589 | +stride=1 | ||
| 590 | +pad=1 | ||
| 591 | +activation=mish | ||
| 592 | + | ||
| 593 | +[convolutional] | ||
| 594 | +batch_normalize=1 | ||
| 595 | +filters=256 | ||
| 596 | +size=3 | ||
| 597 | +stride=1 | ||
| 598 | +pad=1 | ||
| 599 | +activation=mish | ||
| 600 | + | ||
| 601 | +[shortcut] | ||
| 602 | +from=-3 | ||
| 603 | +activation=linear | ||
| 604 | + | ||
| 605 | +[convolutional] | ||
| 606 | +batch_normalize=1 | ||
| 607 | +filters=256 | ||
| 608 | +size=1 | ||
| 609 | +stride=1 | ||
| 610 | +pad=1 | ||
| 611 | +activation=mish | ||
| 612 | + | ||
| 613 | +[route] | ||
| 614 | +layers = -1,-28 | ||
| 615 | + | ||
| 616 | +[convolutional] | ||
| 617 | +batch_normalize=1 | ||
| 618 | +filters=512 | ||
| 619 | +size=1 | ||
| 620 | +stride=1 | ||
| 621 | +pad=1 | ||
| 622 | +activation=mish | ||
| 623 | + | ||
| 624 | +# Downsample | ||
| 625 | + | ||
| 626 | +[convolutional] | ||
| 627 | +batch_normalize=1 | ||
| 628 | +filters=1024 | ||
| 629 | +size=3 | ||
| 630 | +stride=2 | ||
| 631 | +pad=1 | ||
| 632 | +activation=mish | ||
| 633 | + | ||
| 634 | +[convolutional] | ||
| 635 | +batch_normalize=1 | ||
| 636 | +filters=512 | ||
| 637 | +size=1 | ||
| 638 | +stride=1 | ||
| 639 | +pad=1 | ||
| 640 | +activation=mish | ||
| 641 | + | ||
| 642 | +[route] | ||
| 643 | +layers = -2 | ||
| 644 | + | ||
| 645 | +[convolutional] | ||
| 646 | +batch_normalize=1 | ||
| 647 | +filters=512 | ||
| 648 | +size=1 | ||
| 649 | +stride=1 | ||
| 650 | +pad=1 | ||
| 651 | +activation=mish | ||
| 652 | + | ||
| 653 | +[convolutional] | ||
| 654 | +batch_normalize=1 | ||
| 655 | +filters=512 | ||
| 656 | +size=1 | ||
| 657 | +stride=1 | ||
| 658 | +pad=1 | ||
| 659 | +activation=mish | ||
| 660 | + | ||
| 661 | +[convolutional] | ||
| 662 | +batch_normalize=1 | ||
| 663 | +filters=512 | ||
| 664 | +size=3 | ||
| 665 | +stride=1 | ||
| 666 | +pad=1 | ||
| 667 | +activation=mish | ||
| 668 | + | ||
| 669 | +[shortcut] | ||
| 670 | +from=-3 | ||
| 671 | +activation=linear | ||
| 672 | + | ||
| 673 | +[convolutional] | ||
| 674 | +batch_normalize=1 | ||
| 675 | +filters=512 | ||
| 676 | +size=1 | ||
| 677 | +stride=1 | ||
| 678 | +pad=1 | ||
| 679 | +activation=mish | ||
| 680 | + | ||
| 681 | +[convolutional] | ||
| 682 | +batch_normalize=1 | ||
| 683 | +filters=512 | ||
| 684 | +size=3 | ||
| 685 | +stride=1 | ||
| 686 | +pad=1 | ||
| 687 | +activation=mish | ||
| 688 | + | ||
| 689 | +[shortcut] | ||
| 690 | +from=-3 | ||
| 691 | +activation=linear | ||
| 692 | + | ||
| 693 | +[convolutional] | ||
| 694 | +batch_normalize=1 | ||
| 695 | +filters=512 | ||
| 696 | +size=1 | ||
| 697 | +stride=1 | ||
| 698 | +pad=1 | ||
| 699 | +activation=mish | ||
| 700 | + | ||
| 701 | +[convolutional] | ||
| 702 | +batch_normalize=1 | ||
| 703 | +filters=512 | ||
| 704 | +size=3 | ||
| 705 | +stride=1 | ||
| 706 | +pad=1 | ||
| 707 | +activation=mish | ||
| 708 | + | ||
| 709 | +[shortcut] | ||
| 710 | +from=-3 | ||
| 711 | +activation=linear | ||
| 712 | + | ||
| 713 | +[convolutional] | ||
| 714 | +batch_normalize=1 | ||
| 715 | +filters=512 | ||
| 716 | +size=1 | ||
| 717 | +stride=1 | ||
| 718 | +pad=1 | ||
| 719 | +activation=mish | ||
| 720 | + | ||
| 721 | +[convolutional] | ||
| 722 | +batch_normalize=1 | ||
| 723 | +filters=512 | ||
| 724 | +size=3 | ||
| 725 | +stride=1 | ||
| 726 | +pad=1 | ||
| 727 | +activation=mish | ||
| 728 | + | ||
| 729 | +[shortcut] | ||
| 730 | +from=-3 | ||
| 731 | +activation=linear | ||
| 732 | + | ||
| 733 | +[convolutional] | ||
| 734 | +batch_normalize=1 | ||
| 735 | +filters=512 | ||
| 736 | +size=1 | ||
| 737 | +stride=1 | ||
| 738 | +pad=1 | ||
| 739 | +activation=mish | ||
| 740 | + | ||
| 741 | +[route] | ||
| 742 | +layers = -1,-16 | ||
| 743 | + | ||
| 744 | +[convolutional] | ||
| 745 | +batch_normalize=1 | ||
| 746 | +filters=1024 | ||
| 747 | +size=1 | ||
| 748 | +stride=1 | ||
| 749 | +pad=1 | ||
| 750 | +activation=mish | ||
| 751 | + | ||
| 752 | +[avgpool] | ||
| 753 | + | ||
| 754 | +[convolutional] | ||
| 755 | +filters=1000 | ||
| 756 | +size=1 | ||
| 757 | +stride=1 | ||
| 758 | +pad=1 | ||
| 759 | +activation=linear | ||
| 760 | + | ||
| 761 | +[softmax] | ||
| 762 | +groups=1 |
| 1 | +[net] | ||
| 2 | +batch=128 | ||
| 3 | +subdivisions=1 | ||
| 4 | +height=224 | ||
| 5 | +width=224 | ||
| 6 | +channels=3 | ||
| 7 | +momentum=0.9 | ||
| 8 | +decay=0.0005 | ||
| 9 | +max_crop=320 | ||
| 10 | + | ||
| 11 | +learning_rate=0.1 | ||
| 12 | +policy=poly | ||
| 13 | +power=4 | ||
| 14 | +max_batches=1600000 | ||
| 15 | + | ||
| 16 | +[convolutional] | ||
| 17 | +batch_normalize=1 | ||
| 18 | +filters=16 | ||
| 19 | +size=3 | ||
| 20 | +stride=1 | ||
| 21 | +pad=1 | ||
| 22 | +activation=leaky | ||
| 23 | + | ||
| 24 | +[maxpool] | ||
| 25 | +size=2 | ||
| 26 | +stride=2 | ||
| 27 | + | ||
| 28 | +[convolutional] | ||
| 29 | +batch_normalize=1 | ||
| 30 | +filters=32 | ||
| 31 | +size=3 | ||
| 32 | +stride=1 | ||
| 33 | +pad=1 | ||
| 34 | +activation=leaky | ||
| 35 | + | ||
| 36 | +[maxpool] | ||
| 37 | +size=2 | ||
| 38 | +stride=2 | ||
| 39 | + | ||
| 40 | +[convolutional] | ||
| 41 | +batch_normalize=1 | ||
| 42 | +filters=64 | ||
| 43 | +size=3 | ||
| 44 | +stride=1 | ||
| 45 | +pad=1 | ||
| 46 | +activation=leaky | ||
| 47 | + | ||
| 48 | +[maxpool] | ||
| 49 | +size=2 | ||
| 50 | +stride=2 | ||
| 51 | + | ||
| 52 | +[convolutional] | ||
| 53 | +batch_normalize=1 | ||
| 54 | +filters=128 | ||
| 55 | +size=3 | ||
| 56 | +stride=1 | ||
| 57 | +pad=1 | ||
| 58 | +activation=leaky | ||
| 59 | + | ||
| 60 | +[maxpool] | ||
| 61 | +size=2 | ||
| 62 | +stride=2 | ||
| 63 | + | ||
| 64 | +[convolutional] | ||
| 65 | +batch_normalize=1 | ||
| 66 | +filters=256 | ||
| 67 | +size=3 | ||
| 68 | +stride=1 | ||
| 69 | +pad=1 | ||
| 70 | +activation=leaky | ||
| 71 | + | ||
| 72 | +[maxpool] | ||
| 73 | +size=2 | ||
| 74 | +stride=2 | ||
| 75 | + | ||
| 76 | +[convolutional] | ||
| 77 | +batch_normalize=1 | ||
| 78 | +filters=512 | ||
| 79 | +size=3 | ||
| 80 | +stride=1 | ||
| 81 | +pad=1 | ||
| 82 | +activation=leaky | ||
| 83 | + | ||
| 84 | +[maxpool] | ||
| 85 | +size=2 | ||
| 86 | +stride=2 | ||
| 87 | +padding=1 | ||
| 88 | + | ||
| 89 | +[convolutional] | ||
| 90 | +batch_normalize=1 | ||
| 91 | +filters=1024 | ||
| 92 | +size=3 | ||
| 93 | +stride=1 | ||
| 94 | +pad=1 | ||
| 95 | +activation=leaky | ||
| 96 | + | ||
| 97 | +[convolutional] | ||
| 98 | +filters=1000 | ||
| 99 | +size=1 | ||
| 100 | +stride=1 | ||
| 101 | +pad=1 | ||
| 102 | +activation=leaky | ||
| 103 | + | ||
| 104 | +[avgpool] | ||
| 105 | + | ||
| 106 | +[softmax] | ||
| 107 | +groups=1 | ||
| 108 | + | ||
| 109 | +[cost] | ||
| 110 | +type=sse | ||
| 111 | + |
| 1 | +[net] | ||
| 2 | +batch=128 | ||
| 3 | +subdivisions=1 | ||
| 4 | +height=224 | ||
| 5 | +width=224 | ||
| 6 | +channels=3 | ||
| 7 | +momentum=0.9 | ||
| 8 | +decay=0.0005 | ||
| 9 | +max_crop=448 | ||
| 10 | + | ||
| 11 | +learning_rate=0.1 | ||
| 12 | +policy=poly | ||
| 13 | +power=4 | ||
| 14 | +max_batches=1600000 | ||
| 15 | + | ||
| 16 | +[convolutional] | ||
| 17 | +batch_normalize=1 | ||
| 18 | +filters=32 | ||
| 19 | +size=3 | ||
| 20 | +stride=1 | ||
| 21 | +pad=1 | ||
| 22 | +activation=leaky | ||
| 23 | + | ||
| 24 | +[maxpool] | ||
| 25 | +size=2 | ||
| 26 | +stride=2 | ||
| 27 | + | ||
| 28 | +[convolutional] | ||
| 29 | +batch_normalize=1 | ||
| 30 | +filters=64 | ||
| 31 | +size=3 | ||
| 32 | +stride=1 | ||
| 33 | +pad=1 | ||
| 34 | +activation=leaky | ||
| 35 | + | ||
| 36 | +[maxpool] | ||
| 37 | +size=2 | ||
| 38 | +stride=2 | ||
| 39 | + | ||
| 40 | +[convolutional] | ||
| 41 | +batch_normalize=1 | ||
| 42 | +filters=128 | ||
| 43 | +size=3 | ||
| 44 | +stride=1 | ||
| 45 | +pad=1 | ||
| 46 | +activation=leaky | ||
| 47 | + | ||
| 48 | +[convolutional] | ||
| 49 | +batch_normalize=1 | ||
| 50 | +filters=64 | ||
| 51 | +size=1 | ||
| 52 | +stride=1 | ||
| 53 | +pad=1 | ||
| 54 | +activation=leaky | ||
| 55 | + | ||
| 56 | +[convolutional] | ||
| 57 | +batch_normalize=1 | ||
| 58 | +filters=128 | ||
| 59 | +size=3 | ||
| 60 | +stride=1 | ||
| 61 | +pad=1 | ||
| 62 | +activation=leaky | ||
| 63 | + | ||
| 64 | +[maxpool] | ||
| 65 | +size=2 | ||
| 66 | +stride=2 | ||
| 67 | + | ||
| 68 | +[convolutional] | ||
| 69 | +batch_normalize=1 | ||
| 70 | +filters=256 | ||
| 71 | +size=3 | ||
| 72 | +stride=1 | ||
| 73 | +pad=1 | ||
| 74 | +activation=leaky | ||
| 75 | + | ||
| 76 | +[convolutional] | ||
| 77 | +batch_normalize=1 | ||
| 78 | +filters=128 | ||
| 79 | +size=1 | ||
| 80 | +stride=1 | ||
| 81 | +pad=1 | ||
| 82 | +activation=leaky | ||
| 83 | + | ||
| 84 | +[convolutional] | ||
| 85 | +batch_normalize=1 | ||
| 86 | +filters=256 | ||
| 87 | +size=3 | ||
| 88 | +stride=1 | ||
| 89 | +pad=1 | ||
| 90 | +activation=leaky | ||
| 91 | + | ||
| 92 | +[maxpool] | ||
| 93 | +size=2 | ||
| 94 | +stride=2 | ||
| 95 | + | ||
| 96 | +[convolutional] | ||
| 97 | +batch_normalize=1 | ||
| 98 | +filters=512 | ||
| 99 | +size=3 | ||
| 100 | +stride=1 | ||
| 101 | +pad=1 | ||
| 102 | +activation=leaky | ||
| 103 | + | ||
| 104 | +[convolutional] | ||
| 105 | +batch_normalize=1 | ||
| 106 | +filters=256 | ||
| 107 | +size=1 | ||
| 108 | +stride=1 | ||
| 109 | +pad=1 | ||
| 110 | +activation=leaky | ||
| 111 | + | ||
| 112 | +[convolutional] | ||
| 113 | +batch_normalize=1 | ||
| 114 | +filters=512 | ||
| 115 | +size=3 | ||
| 116 | +stride=1 | ||
| 117 | +pad=1 | ||
| 118 | +activation=leaky | ||
| 119 | + | ||
| 120 | +[convolutional] | ||
| 121 | +batch_normalize=1 | ||
| 122 | +filters=256 | ||
| 123 | +size=1 | ||
| 124 | +stride=1 | ||
| 125 | +pad=1 | ||
| 126 | +activation=leaky | ||
| 127 | + | ||
| 128 | +[convolutional] | ||
| 129 | +batch_normalize=1 | ||
| 130 | +filters=512 | ||
| 131 | +size=3 | ||
| 132 | +stride=1 | ||
| 133 | +pad=1 | ||
| 134 | +activation=leaky | ||
| 135 | + | ||
| 136 | +[maxpool] | ||
| 137 | +size=2 | ||
| 138 | +stride=2 | ||
| 139 | + | ||
| 140 | +[convolutional] | ||
| 141 | +batch_normalize=1 | ||
| 142 | +filters=1024 | ||
| 143 | +size=3 | ||
| 144 | +stride=1 | ||
| 145 | +pad=1 | ||
| 146 | +activation=leaky | ||
| 147 | + | ||
| 148 | +[convolutional] | ||
| 149 | +batch_normalize=1 | ||
| 150 | +filters=512 | ||
| 151 | +size=1 | ||
| 152 | +stride=1 | ||
| 153 | +pad=1 | ||
| 154 | +activation=leaky | ||
| 155 | + | ||
| 156 | +[convolutional] | ||
| 157 | +batch_normalize=1 | ||
| 158 | +filters=1024 | ||
| 159 | +size=3 | ||
| 160 | +stride=1 | ||
| 161 | +pad=1 | ||
| 162 | +activation=leaky | ||
| 163 | + | ||
| 164 | +[convolutional] | ||
| 165 | +batch_normalize=1 | ||
| 166 | +filters=512 | ||
| 167 | +size=1 | ||
| 168 | +stride=1 | ||
| 169 | +pad=1 | ||
| 170 | +activation=leaky | ||
| 171 | + | ||
| 172 | +[convolutional] | ||
| 173 | +batch_normalize=1 | ||
| 174 | +filters=1024 | ||
| 175 | +size=3 | ||
| 176 | +stride=1 | ||
| 177 | +pad=1 | ||
| 178 | +activation=leaky | ||
| 179 | + | ||
| 180 | +[convolutional] | ||
| 181 | +filters=1000 | ||
| 182 | +size=1 | ||
| 183 | +stride=1 | ||
| 184 | +pad=1 | ||
| 185 | +activation=linear | ||
| 186 | + | ||
| 187 | +[avgpool] | ||
| 188 | + | ||
| 189 | +[softmax] | ||
| 190 | +groups=1 | ||
| 191 | + | ||
| 192 | +[cost] | ||
| 193 | +type=sse | ||
| 194 | + |
| 1 | +[net] | ||
| 2 | +#batch=128 | ||
| 3 | +#subdivisions=4 | ||
| 4 | +batch=1 | ||
| 5 | +subdivisions=1 | ||
| 6 | +height=448 | ||
| 7 | +width=448 | ||
| 8 | +max_crop=512 | ||
| 9 | +channels=3 | ||
| 10 | +momentum=0.9 | ||
| 11 | +decay=0.0005 | ||
| 12 | + | ||
| 13 | +learning_rate=0.001 | ||
| 14 | +policy=poly | ||
| 15 | +power=4 | ||
| 16 | +max_batches=100000 | ||
| 17 | + | ||
| 18 | +angle=7 | ||
| 19 | +hue = .1 | ||
| 20 | +saturation=.75 | ||
| 21 | +exposure=.75 | ||
| 22 | +aspect=.75 | ||
| 23 | + | ||
| 24 | +[convolutional] | ||
| 25 | +batch_normalize=1 | ||
| 26 | +filters=32 | ||
| 27 | +size=3 | ||
| 28 | +stride=1 | ||
| 29 | +pad=1 | ||
| 30 | +activation=leaky | ||
| 31 | + | ||
| 32 | +[maxpool] | ||
| 33 | +size=2 | ||
| 34 | +stride=2 | ||
| 35 | + | ||
| 36 | +[convolutional] | ||
| 37 | +batch_normalize=1 | ||
| 38 | +filters=64 | ||
| 39 | +size=3 | ||
| 40 | +stride=1 | ||
| 41 | +pad=1 | ||
| 42 | +activation=leaky | ||
| 43 | + | ||
| 44 | +[maxpool] | ||
| 45 | +size=2 | ||
| 46 | +stride=2 | ||
| 47 | + | ||
| 48 | +[convolutional] | ||
| 49 | +batch_normalize=1 | ||
| 50 | +filters=128 | ||
| 51 | +size=3 | ||
| 52 | +stride=1 | ||
| 53 | +pad=1 | ||
| 54 | +activation=leaky | ||
| 55 | + | ||
| 56 | +[convolutional] | ||
| 57 | +batch_normalize=1 | ||
| 58 | +filters=64 | ||
| 59 | +size=1 | ||
| 60 | +stride=1 | ||
| 61 | +pad=1 | ||
| 62 | +activation=leaky | ||
| 63 | + | ||
| 64 | +[convolutional] | ||
| 65 | +batch_normalize=1 | ||
| 66 | +filters=128 | ||
| 67 | +size=3 | ||
| 68 | +stride=1 | ||
| 69 | +pad=1 | ||
| 70 | +activation=leaky | ||
| 71 | + | ||
| 72 | +[maxpool] | ||
| 73 | +size=2 | ||
| 74 | +stride=2 | ||
| 75 | + | ||
| 76 | +[convolutional] | ||
| 77 | +batch_normalize=1 | ||
| 78 | +filters=256 | ||
| 79 | +size=3 | ||
| 80 | +stride=1 | ||
| 81 | +pad=1 | ||
| 82 | +activation=leaky | ||
| 83 | + | ||
| 84 | +[convolutional] | ||
| 85 | +batch_normalize=1 | ||
| 86 | +filters=128 | ||
| 87 | +size=1 | ||
| 88 | +stride=1 | ||
| 89 | +pad=1 | ||
| 90 | +activation=leaky | ||
| 91 | + | ||
| 92 | +[convolutional] | ||
| 93 | +batch_normalize=1 | ||
| 94 | +filters=256 | ||
| 95 | +size=3 | ||
| 96 | +stride=1 | ||
| 97 | +pad=1 | ||
| 98 | +activation=leaky | ||
| 99 | + | ||
| 100 | +[maxpool] | ||
| 101 | +size=2 | ||
| 102 | +stride=2 | ||
| 103 | + | ||
| 104 | +[convolutional] | ||
| 105 | +batch_normalize=1 | ||
| 106 | +filters=512 | ||
| 107 | +size=3 | ||
| 108 | +stride=1 | ||
| 109 | +pad=1 | ||
| 110 | +activation=leaky | ||
| 111 | + | ||
| 112 | +[convolutional] | ||
| 113 | +batch_normalize=1 | ||
| 114 | +filters=256 | ||
| 115 | +size=1 | ||
| 116 | +stride=1 | ||
| 117 | +pad=1 | ||
| 118 | +activation=leaky | ||
| 119 | + | ||
| 120 | +[convolutional] | ||
| 121 | +batch_normalize=1 | ||
| 122 | +filters=512 | ||
| 123 | +size=3 | ||
| 124 | +stride=1 | ||
| 125 | +pad=1 | ||
| 126 | +activation=leaky | ||
| 127 | + | ||
| 128 | +[convolutional] | ||
| 129 | +batch_normalize=1 | ||
| 130 | +filters=256 | ||
| 131 | +size=1 | ||
| 132 | +stride=1 | ||
| 133 | +pad=1 | ||
| 134 | +activation=leaky | ||
| 135 | + | ||
| 136 | +[convolutional] | ||
| 137 | +batch_normalize=1 | ||
| 138 | +filters=512 | ||
| 139 | +size=3 | ||
| 140 | +stride=1 | ||
| 141 | +pad=1 | ||
| 142 | +activation=leaky | ||
| 143 | + | ||
| 144 | +[maxpool] | ||
| 145 | +size=2 | ||
| 146 | +stride=2 | ||
| 147 | + | ||
| 148 | +[convolutional] | ||
| 149 | +batch_normalize=1 | ||
| 150 | +filters=1024 | ||
| 151 | +size=3 | ||
| 152 | +stride=1 | ||
| 153 | +pad=1 | ||
| 154 | +activation=leaky | ||
| 155 | + | ||
| 156 | +[convolutional] | ||
| 157 | +batch_normalize=1 | ||
| 158 | +filters=512 | ||
| 159 | +size=1 | ||
| 160 | +stride=1 | ||
| 161 | +pad=1 | ||
| 162 | +activation=leaky | ||
| 163 | + | ||
| 164 | +[convolutional] | ||
| 165 | +batch_normalize=1 | ||
| 166 | +filters=1024 | ||
| 167 | +size=3 | ||
| 168 | +stride=1 | ||
| 169 | +pad=1 | ||
| 170 | +activation=leaky | ||
| 171 | + | ||
| 172 | +[convolutional] | ||
| 173 | +batch_normalize=1 | ||
| 174 | +filters=512 | ||
| 175 | +size=1 | ||
| 176 | +stride=1 | ||
| 177 | +pad=1 | ||
| 178 | +activation=leaky | ||
| 179 | + | ||
| 180 | +[convolutional] | ||
| 181 | +batch_normalize=1 | ||
| 182 | +filters=1024 | ||
| 183 | +size=3 | ||
| 184 | +stride=1 | ||
| 185 | +pad=1 | ||
| 186 | +activation=leaky | ||
| 187 | + | ||
| 188 | +[convolutional] | ||
| 189 | +filters=1000 | ||
| 190 | +size=1 | ||
| 191 | +stride=1 | ||
| 192 | +pad=1 | ||
| 193 | +activation=linear | ||
| 194 | + | ||
| 195 | +[avgpool] | ||
| 196 | + | ||
| 197 | +[softmax] | ||
| 198 | +groups=1 | ||
| 199 | + | ||
| 200 | +[cost] | ||
| 201 | +type=sse | ||
| 202 | + |
| 1 | +[net] | ||
| 2 | +# Training | ||
| 3 | +batch=128 | ||
| 4 | +subdivisions=8 | ||
| 5 | + | ||
| 6 | +# Testing | ||
| 7 | +#batch=1 | ||
| 8 | +#subdivisions=1 | ||
| 9 | + | ||
| 10 | +height=256 | ||
| 11 | +width=256 | ||
| 12 | +channels=3 | ||
| 13 | +min_crop=128 | ||
| 14 | +max_crop=448 | ||
| 15 | + | ||
| 16 | +burn_in=1000 | ||
| 17 | +learning_rate=0.1 | ||
| 18 | +policy=poly | ||
| 19 | +power=4 | ||
| 20 | +max_batches=800000 | ||
| 21 | +momentum=0.9 | ||
| 22 | +decay=0.0005 | ||
| 23 | + | ||
| 24 | +angle=7 | ||
| 25 | +hue=.1 | ||
| 26 | +saturation=.75 | ||
| 27 | +exposure=.75 | ||
| 28 | +aspect=.75 | ||
| 29 | + | ||
| 30 | + | ||
| 31 | +[convolutional] | ||
| 32 | +batch_normalize=1 | ||
| 33 | +filters=32 | ||
| 34 | +size=3 | ||
| 35 | +stride=1 | ||
| 36 | +pad=1 | ||
| 37 | +activation=leaky | ||
| 38 | + | ||
| 39 | +# Downsample | ||
| 40 | + | ||
| 41 | +[convolutional] | ||
| 42 | +batch_normalize=1 | ||
| 43 | +filters=64 | ||
| 44 | +size=3 | ||
| 45 | +stride=2 | ||
| 46 | +pad=1 | ||
| 47 | +activation=leaky | ||
| 48 | + | ||
| 49 | +[convolutional] | ||
| 50 | +batch_normalize=1 | ||
| 51 | +filters=32 | ||
| 52 | +size=1 | ||
| 53 | +stride=1 | ||
| 54 | +pad=1 | ||
| 55 | +activation=leaky | ||
| 56 | + | ||
| 57 | +[convolutional] | ||
| 58 | +batch_normalize=1 | ||
| 59 | +filters=64 | ||
| 60 | +size=3 | ||
| 61 | +stride=1 | ||
| 62 | +pad=1 | ||
| 63 | +activation=leaky | ||
| 64 | + | ||
| 65 | +[shortcut] | ||
| 66 | +from=-3 | ||
| 67 | +activation=linear | ||
| 68 | + | ||
| 69 | +# Downsample | ||
| 70 | + | ||
| 71 | +[convolutional] | ||
| 72 | +batch_normalize=1 | ||
| 73 | +filters=128 | ||
| 74 | +size=3 | ||
| 75 | +stride=2 | ||
| 76 | +pad=1 | ||
| 77 | +activation=leaky | ||
| 78 | + | ||
| 79 | +[convolutional] | ||
| 80 | +batch_normalize=1 | ||
| 81 | +filters=64 | ||
| 82 | +size=1 | ||
| 83 | +stride=1 | ||
| 84 | +pad=1 | ||
| 85 | +activation=leaky | ||
| 86 | + | ||
| 87 | +[convolutional] | ||
| 88 | +batch_normalize=1 | ||
| 89 | +filters=128 | ||
| 90 | +size=3 | ||
| 91 | +stride=1 | ||
| 92 | +pad=1 | ||
| 93 | +activation=leaky | ||
| 94 | + | ||
| 95 | +[shortcut] | ||
| 96 | +from=-3 | ||
| 97 | +activation=linear | ||
| 98 | + | ||
| 99 | +[convolutional] | ||
| 100 | +batch_normalize=1 | ||
| 101 | +filters=64 | ||
| 102 | +size=1 | ||
| 103 | +stride=1 | ||
| 104 | +pad=1 | ||
| 105 | +activation=leaky | ||
| 106 | + | ||
| 107 | +[convolutional] | ||
| 108 | +batch_normalize=1 | ||
| 109 | +filters=128 | ||
| 110 | +size=3 | ||
| 111 | +stride=1 | ||
| 112 | +pad=1 | ||
| 113 | +activation=leaky | ||
| 114 | + | ||
| 115 | +[shortcut] | ||
| 116 | +from=-3 | ||
| 117 | +activation=linear | ||
| 118 | + | ||
| 119 | +# Downsample | ||
| 120 | + | ||
| 121 | +[convolutional] | ||
| 122 | +batch_normalize=1 | ||
| 123 | +filters=256 | ||
| 124 | +size=3 | ||
| 125 | +stride=2 | ||
| 126 | +pad=1 | ||
| 127 | +activation=leaky | ||
| 128 | + | ||
| 129 | +[convolutional] | ||
| 130 | +batch_normalize=1 | ||
| 131 | +filters=128 | ||
| 132 | +size=1 | ||
| 133 | +stride=1 | ||
| 134 | +pad=1 | ||
| 135 | +activation=leaky | ||
| 136 | + | ||
| 137 | +[convolutional] | ||
| 138 | +batch_normalize=1 | ||
| 139 | +filters=256 | ||
| 140 | +size=3 | ||
| 141 | +stride=1 | ||
| 142 | +pad=1 | ||
| 143 | +activation=leaky | ||
| 144 | + | ||
| 145 | +[shortcut] | ||
| 146 | +from=-3 | ||
| 147 | +activation=linear | ||
| 148 | + | ||
| 149 | +[convolutional] | ||
| 150 | +batch_normalize=1 | ||
| 151 | +filters=128 | ||
| 152 | +size=1 | ||
| 153 | +stride=1 | ||
| 154 | +pad=1 | ||
| 155 | +activation=leaky | ||
| 156 | + | ||
| 157 | +[convolutional] | ||
| 158 | +batch_normalize=1 | ||
| 159 | +filters=256 | ||
| 160 | +size=3 | ||
| 161 | +stride=1 | ||
| 162 | +pad=1 | ||
| 163 | +activation=leaky | ||
| 164 | + | ||
| 165 | +[shortcut] | ||
| 166 | +from=-3 | ||
| 167 | +activation=linear | ||
| 168 | + | ||
| 169 | +[convolutional] | ||
| 170 | +batch_normalize=1 | ||
| 171 | +filters=128 | ||
| 172 | +size=1 | ||
| 173 | +stride=1 | ||
| 174 | +pad=1 | ||
| 175 | +activation=leaky | ||
| 176 | + | ||
| 177 | +[convolutional] | ||
| 178 | +batch_normalize=1 | ||
| 179 | +filters=256 | ||
| 180 | +size=3 | ||
| 181 | +stride=1 | ||
| 182 | +pad=1 | ||
| 183 | +activation=leaky | ||
| 184 | + | ||
| 185 | +[shortcut] | ||
| 186 | +from=-3 | ||
| 187 | +activation=linear | ||
| 188 | + | ||
| 189 | +[convolutional] | ||
| 190 | +batch_normalize=1 | ||
| 191 | +filters=128 | ||
| 192 | +size=1 | ||
| 193 | +stride=1 | ||
| 194 | +pad=1 | ||
| 195 | +activation=leaky | ||
| 196 | + | ||
| 197 | +[convolutional] | ||
| 198 | +batch_normalize=1 | ||
| 199 | +filters=256 | ||
| 200 | +size=3 | ||
| 201 | +stride=1 | ||
| 202 | +pad=1 | ||
| 203 | +activation=leaky | ||
| 204 | + | ||
| 205 | +[shortcut] | ||
| 206 | +from=-3 | ||
| 207 | +activation=linear | ||
| 208 | + | ||
| 209 | + | ||
| 210 | +[convolutional] | ||
| 211 | +batch_normalize=1 | ||
| 212 | +filters=128 | ||
| 213 | +size=1 | ||
| 214 | +stride=1 | ||
| 215 | +pad=1 | ||
| 216 | +activation=leaky | ||
| 217 | + | ||
| 218 | +[convolutional] | ||
| 219 | +batch_normalize=1 | ||
| 220 | +filters=256 | ||
| 221 | +size=3 | ||
| 222 | +stride=1 | ||
| 223 | +pad=1 | ||
| 224 | +activation=leaky | ||
| 225 | + | ||
| 226 | +[shortcut] | ||
| 227 | +from=-3 | ||
| 228 | +activation=linear | ||
| 229 | + | ||
| 230 | +[convolutional] | ||
| 231 | +batch_normalize=1 | ||
| 232 | +filters=128 | ||
| 233 | +size=1 | ||
| 234 | +stride=1 | ||
| 235 | +pad=1 | ||
| 236 | +activation=leaky | ||
| 237 | + | ||
| 238 | +[convolutional] | ||
| 239 | +batch_normalize=1 | ||
| 240 | +filters=256 | ||
| 241 | +size=3 | ||
| 242 | +stride=1 | ||
| 243 | +pad=1 | ||
| 244 | +activation=leaky | ||
| 245 | + | ||
| 246 | +[shortcut] | ||
| 247 | +from=-3 | ||
| 248 | +activation=linear | ||
| 249 | + | ||
| 250 | +[convolutional] | ||
| 251 | +batch_normalize=1 | ||
| 252 | +filters=128 | ||
| 253 | +size=1 | ||
| 254 | +stride=1 | ||
| 255 | +pad=1 | ||
| 256 | +activation=leaky | ||
| 257 | + | ||
| 258 | +[convolutional] | ||
| 259 | +batch_normalize=1 | ||
| 260 | +filters=256 | ||
| 261 | +size=3 | ||
| 262 | +stride=1 | ||
| 263 | +pad=1 | ||
| 264 | +activation=leaky | ||
| 265 | + | ||
| 266 | +[shortcut] | ||
| 267 | +from=-3 | ||
| 268 | +activation=linear | ||
| 269 | + | ||
| 270 | +[convolutional] | ||
| 271 | +batch_normalize=1 | ||
| 272 | +filters=128 | ||
| 273 | +size=1 | ||
| 274 | +stride=1 | ||
| 275 | +pad=1 | ||
| 276 | +activation=leaky | ||
| 277 | + | ||
| 278 | +[convolutional] | ||
| 279 | +batch_normalize=1 | ||
| 280 | +filters=256 | ||
| 281 | +size=3 | ||
| 282 | +stride=1 | ||
| 283 | +pad=1 | ||
| 284 | +activation=leaky | ||
| 285 | + | ||
| 286 | +[shortcut] | ||
| 287 | +from=-3 | ||
| 288 | +activation=linear | ||
| 289 | + | ||
| 290 | +# Downsample | ||
| 291 | + | ||
| 292 | +[convolutional] | ||
| 293 | +batch_normalize=1 | ||
| 294 | +filters=512 | ||
| 295 | +size=3 | ||
| 296 | +stride=2 | ||
| 297 | +pad=1 | ||
| 298 | +activation=leaky | ||
| 299 | + | ||
| 300 | +[convolutional] | ||
| 301 | +batch_normalize=1 | ||
| 302 | +filters=256 | ||
| 303 | +size=1 | ||
| 304 | +stride=1 | ||
| 305 | +pad=1 | ||
| 306 | +activation=leaky | ||
| 307 | + | ||
| 308 | +[convolutional] | ||
| 309 | +batch_normalize=1 | ||
| 310 | +filters=512 | ||
| 311 | +size=3 | ||
| 312 | +stride=1 | ||
| 313 | +pad=1 | ||
| 314 | +activation=leaky | ||
| 315 | + | ||
| 316 | +[shortcut] | ||
| 317 | +from=-3 | ||
| 318 | +activation=linear | ||
| 319 | + | ||
| 320 | + | ||
| 321 | +[convolutional] | ||
| 322 | +batch_normalize=1 | ||
| 323 | +filters=256 | ||
| 324 | +size=1 | ||
| 325 | +stride=1 | ||
| 326 | +pad=1 | ||
| 327 | +activation=leaky | ||
| 328 | + | ||
| 329 | +[convolutional] | ||
| 330 | +batch_normalize=1 | ||
| 331 | +filters=512 | ||
| 332 | +size=3 | ||
| 333 | +stride=1 | ||
| 334 | +pad=1 | ||
| 335 | +activation=leaky | ||
| 336 | + | ||
| 337 | +[shortcut] | ||
| 338 | +from=-3 | ||
| 339 | +activation=linear | ||
| 340 | + | ||
| 341 | + | ||
| 342 | +[convolutional] | ||
| 343 | +batch_normalize=1 | ||
| 344 | +filters=256 | ||
| 345 | +size=1 | ||
| 346 | +stride=1 | ||
| 347 | +pad=1 | ||
| 348 | +activation=leaky | ||
| 349 | + | ||
| 350 | +[convolutional] | ||
| 351 | +batch_normalize=1 | ||
| 352 | +filters=512 | ||
| 353 | +size=3 | ||
| 354 | +stride=1 | ||
| 355 | +pad=1 | ||
| 356 | +activation=leaky | ||
| 357 | + | ||
| 358 | +[shortcut] | ||
| 359 | +from=-3 | ||
| 360 | +activation=linear | ||
| 361 | + | ||
| 362 | + | ||
| 363 | +[convolutional] | ||
| 364 | +batch_normalize=1 | ||
| 365 | +filters=256 | ||
| 366 | +size=1 | ||
| 367 | +stride=1 | ||
| 368 | +pad=1 | ||
| 369 | +activation=leaky | ||
| 370 | + | ||
| 371 | +[convolutional] | ||
| 372 | +batch_normalize=1 | ||
| 373 | +filters=512 | ||
| 374 | +size=3 | ||
| 375 | +stride=1 | ||
| 376 | +pad=1 | ||
| 377 | +activation=leaky | ||
| 378 | + | ||
| 379 | +[shortcut] | ||
| 380 | +from=-3 | ||
| 381 | +activation=linear | ||
| 382 | + | ||
| 383 | +[convolutional] | ||
| 384 | +batch_normalize=1 | ||
| 385 | +filters=256 | ||
| 386 | +size=1 | ||
| 387 | +stride=1 | ||
| 388 | +pad=1 | ||
| 389 | +activation=leaky | ||
| 390 | + | ||
| 391 | +[convolutional] | ||
| 392 | +batch_normalize=1 | ||
| 393 | +filters=512 | ||
| 394 | +size=3 | ||
| 395 | +stride=1 | ||
| 396 | +pad=1 | ||
| 397 | +activation=leaky | ||
| 398 | + | ||
| 399 | +[shortcut] | ||
| 400 | +from=-3 | ||
| 401 | +activation=linear | ||
| 402 | + | ||
| 403 | + | ||
| 404 | +[convolutional] | ||
| 405 | +batch_normalize=1 | ||
| 406 | +filters=256 | ||
| 407 | +size=1 | ||
| 408 | +stride=1 | ||
| 409 | +pad=1 | ||
| 410 | +activation=leaky | ||
| 411 | + | ||
| 412 | +[convolutional] | ||
| 413 | +batch_normalize=1 | ||
| 414 | +filters=512 | ||
| 415 | +size=3 | ||
| 416 | +stride=1 | ||
| 417 | +pad=1 | ||
| 418 | +activation=leaky | ||
| 419 | + | ||
| 420 | +[shortcut] | ||
| 421 | +from=-3 | ||
| 422 | +activation=linear | ||
| 423 | + | ||
| 424 | + | ||
| 425 | +[convolutional] | ||
| 426 | +batch_normalize=1 | ||
| 427 | +filters=256 | ||
| 428 | +size=1 | ||
| 429 | +stride=1 | ||
| 430 | +pad=1 | ||
| 431 | +activation=leaky | ||
| 432 | + | ||
| 433 | +[convolutional] | ||
| 434 | +batch_normalize=1 | ||
| 435 | +filters=512 | ||
| 436 | +size=3 | ||
| 437 | +stride=1 | ||
| 438 | +pad=1 | ||
| 439 | +activation=leaky | ||
| 440 | + | ||
| 441 | +[shortcut] | ||
| 442 | +from=-3 | ||
| 443 | +activation=linear | ||
| 444 | + | ||
| 445 | +[convolutional] | ||
| 446 | +batch_normalize=1 | ||
| 447 | +filters=256 | ||
| 448 | +size=1 | ||
| 449 | +stride=1 | ||
| 450 | +pad=1 | ||
| 451 | +activation=leaky | ||
| 452 | + | ||
| 453 | +[convolutional] | ||
| 454 | +batch_normalize=1 | ||
| 455 | +filters=512 | ||
| 456 | +size=3 | ||
| 457 | +stride=1 | ||
| 458 | +pad=1 | ||
| 459 | +activation=leaky | ||
| 460 | + | ||
| 461 | +[shortcut] | ||
| 462 | +from=-3 | ||
| 463 | +activation=linear | ||
| 464 | + | ||
| 465 | +# Downsample | ||
| 466 | + | ||
| 467 | +[convolutional] | ||
| 468 | +batch_normalize=1 | ||
| 469 | +filters=1024 | ||
| 470 | +size=3 | ||
| 471 | +stride=2 | ||
| 472 | +pad=1 | ||
| 473 | +activation=leaky | ||
| 474 | + | ||
| 475 | +[convolutional] | ||
| 476 | +batch_normalize=1 | ||
| 477 | +filters=512 | ||
| 478 | +size=1 | ||
| 479 | +stride=1 | ||
| 480 | +pad=1 | ||
| 481 | +activation=leaky | ||
| 482 | + | ||
| 483 | +[convolutional] | ||
| 484 | +batch_normalize=1 | ||
| 485 | +filters=1024 | ||
| 486 | +size=3 | ||
| 487 | +stride=1 | ||
| 488 | +pad=1 | ||
| 489 | +activation=leaky | ||
| 490 | + | ||
| 491 | +[shortcut] | ||
| 492 | +from=-3 | ||
| 493 | +activation=linear | ||
| 494 | + | ||
| 495 | +[convolutional] | ||
| 496 | +batch_normalize=1 | ||
| 497 | +filters=512 | ||
| 498 | +size=1 | ||
| 499 | +stride=1 | ||
| 500 | +pad=1 | ||
| 501 | +activation=leaky | ||
| 502 | + | ||
| 503 | +[convolutional] | ||
| 504 | +batch_normalize=1 | ||
| 505 | +filters=1024 | ||
| 506 | +size=3 | ||
| 507 | +stride=1 | ||
| 508 | +pad=1 | ||
| 509 | +activation=leaky | ||
| 510 | + | ||
| 511 | +[shortcut] | ||
| 512 | +from=-3 | ||
| 513 | +activation=linear | ||
| 514 | + | ||
| 515 | +[convolutional] | ||
| 516 | +batch_normalize=1 | ||
| 517 | +filters=512 | ||
| 518 | +size=1 | ||
| 519 | +stride=1 | ||
| 520 | +pad=1 | ||
| 521 | +activation=leaky | ||
| 522 | + | ||
| 523 | +[convolutional] | ||
| 524 | +batch_normalize=1 | ||
| 525 | +filters=1024 | ||
| 526 | +size=3 | ||
| 527 | +stride=1 | ||
| 528 | +pad=1 | ||
| 529 | +activation=leaky | ||
| 530 | + | ||
| 531 | +[shortcut] | ||
| 532 | +from=-3 | ||
| 533 | +activation=linear | ||
| 534 | + | ||
| 535 | +[convolutional] | ||
| 536 | +batch_normalize=1 | ||
| 537 | +filters=512 | ||
| 538 | +size=1 | ||
| 539 | +stride=1 | ||
| 540 | +pad=1 | ||
| 541 | +activation=leaky | ||
| 542 | + | ||
| 543 | +[convolutional] | ||
| 544 | +batch_normalize=1 | ||
| 545 | +filters=1024 | ||
| 546 | +size=3 | ||
| 547 | +stride=1 | ||
| 548 | +pad=1 | ||
| 549 | +activation=leaky | ||
| 550 | + | ||
| 551 | +[shortcut] | ||
| 552 | +from=-3 | ||
| 553 | +activation=linear | ||
| 554 | + | ||
| 555 | +[avgpool] | ||
| 556 | + | ||
| 557 | +[convolutional] | ||
| 558 | +filters=1000 | ||
| 559 | +size=1 | ||
| 560 | +stride=1 | ||
| 561 | +pad=1 | ||
| 562 | +activation=linear | ||
| 563 | + | ||
| 564 | +[softmax] | ||
| 565 | +groups=1 | ||
| 566 | + |
| 1 | +[net] | ||
| 2 | +# Training - start training with darknet53.weights | ||
| 3 | +batch=120 | ||
| 4 | +subdivisions=20 | ||
| 5 | + | ||
| 6 | +# Testing | ||
| 7 | +#batch=1 | ||
| 8 | +#subdivisions=1 | ||
| 9 | + | ||
| 10 | +height=448 | ||
| 11 | +width=448 | ||
| 12 | +channels=3 | ||
| 13 | +min_crop=448 | ||
| 14 | +max_crop=512 | ||
| 15 | + | ||
| 16 | +burn_in=1000 | ||
| 17 | +learning_rate=0.1 | ||
| 18 | +policy=poly | ||
| 19 | +power=4 | ||
| 20 | +max_batches=100000 | ||
| 21 | +momentum=0.9 | ||
| 22 | +decay=0.0005 | ||
| 23 | + | ||
| 24 | + | ||
| 25 | +[convolutional] | ||
| 26 | +batch_normalize=1 | ||
| 27 | +filters=32 | ||
| 28 | +size=3 | ||
| 29 | +stride=1 | ||
| 30 | +pad=1 | ||
| 31 | +activation=leaky | ||
| 32 | + | ||
| 33 | +# Downsample | ||
| 34 | + | ||
| 35 | +[convolutional] | ||
| 36 | +xnor=1 | ||
| 37 | +batch_normalize=1 | ||
| 38 | +filters=64 | ||
| 39 | +size=3 | ||
| 40 | +stride=2 | ||
| 41 | +pad=1 | ||
| 42 | +activation=leaky | ||
| 43 | + | ||
| 44 | +[convolutional] | ||
| 45 | +xnor=1 | ||
| 46 | +batch_normalize=1 | ||
| 47 | +filters=32 | ||
| 48 | +size=1 | ||
| 49 | +stride=1 | ||
| 50 | +pad=1 | ||
| 51 | +activation=leaky | ||
| 52 | + | ||
| 53 | +[convolutional] | ||
| 54 | +xnor=1 | ||
| 55 | +batch_normalize=1 | ||
| 56 | +filters=64 | ||
| 57 | +size=3 | ||
| 58 | +stride=1 | ||
| 59 | +pad=1 | ||
| 60 | +activation=leaky | ||
| 61 | + | ||
| 62 | +[shortcut] | ||
| 63 | +from=-3 | ||
| 64 | +activation=linear | ||
| 65 | + | ||
| 66 | +# Downsample | ||
| 67 | + | ||
| 68 | +[convolutional] | ||
| 69 | +xnor=1 | ||
| 70 | +batch_normalize=1 | ||
| 71 | +filters=128 | ||
| 72 | +size=3 | ||
| 73 | +stride=2 | ||
| 74 | +pad=1 | ||
| 75 | +activation=leaky | ||
| 76 | + | ||
| 77 | +[convolutional] | ||
| 78 | +xnor=1 | ||
| 79 | +batch_normalize=1 | ||
| 80 | +filters=64 | ||
| 81 | +size=1 | ||
| 82 | +stride=1 | ||
| 83 | +pad=1 | ||
| 84 | +activation=leaky | ||
| 85 | + | ||
| 86 | +[convolutional] | ||
| 87 | +xnor=1 | ||
| 88 | +batch_normalize=1 | ||
| 89 | +filters=128 | ||
| 90 | +size=3 | ||
| 91 | +stride=1 | ||
| 92 | +pad=1 | ||
| 93 | +activation=leaky | ||
| 94 | + | ||
| 95 | +[shortcut] | ||
| 96 | +from=-3 | ||
| 97 | +activation=linear | ||
| 98 | + | ||
| 99 | +[convolutional] | ||
| 100 | +xnor=1 | ||
| 101 | +batch_normalize=1 | ||
| 102 | +filters=64 | ||
| 103 | +size=1 | ||
| 104 | +stride=1 | ||
| 105 | +pad=1 | ||
| 106 | +activation=leaky | ||
| 107 | + | ||
| 108 | +[convolutional] | ||
| 109 | +xnor=1 | ||
| 110 | +batch_normalize=1 | ||
| 111 | +filters=128 | ||
| 112 | +size=3 | ||
| 113 | +stride=1 | ||
| 114 | +pad=1 | ||
| 115 | +activation=leaky | ||
| 116 | + | ||
| 117 | +[shortcut] | ||
| 118 | +from=-3 | ||
| 119 | +activation=linear | ||
| 120 | + | ||
| 121 | +# Downsample | ||
| 122 | + | ||
| 123 | +[convolutional] | ||
| 124 | +xnor=1 | ||
| 125 | +batch_normalize=1 | ||
| 126 | +filters=256 | ||
| 127 | +size=3 | ||
| 128 | +stride=2 | ||
| 129 | +pad=1 | ||
| 130 | +activation=leaky | ||
| 131 | + | ||
| 132 | +[convolutional] | ||
| 133 | +xnor=1 | ||
| 134 | +batch_normalize=1 | ||
| 135 | +filters=128 | ||
| 136 | +size=1 | ||
| 137 | +stride=1 | ||
| 138 | +pad=1 | ||
| 139 | +activation=leaky | ||
| 140 | + | ||
| 141 | +[convolutional] | ||
| 142 | +xnor=1 | ||
| 143 | +batch_normalize=1 | ||
| 144 | +filters=256 | ||
| 145 | +size=3 | ||
| 146 | +stride=1 | ||
| 147 | +pad=1 | ||
| 148 | +activation=leaky | ||
| 149 | + | ||
| 150 | +[shortcut] | ||
| 151 | +from=-3 | ||
| 152 | +activation=linear | ||
| 153 | + | ||
| 154 | +[convolutional] | ||
| 155 | +xnor=1 | ||
| 156 | +batch_normalize=1 | ||
| 157 | +filters=128 | ||
| 158 | +size=1 | ||
| 159 | +stride=1 | ||
| 160 | +pad=1 | ||
| 161 | +activation=leaky | ||
| 162 | + | ||
| 163 | +[convolutional] | ||
| 164 | +xnor=1 | ||
| 165 | +batch_normalize=1 | ||
| 166 | +filters=256 | ||
| 167 | +size=3 | ||
| 168 | +stride=1 | ||
| 169 | +pad=1 | ||
| 170 | +activation=leaky | ||
| 171 | + | ||
| 172 | +[shortcut] | ||
| 173 | +from=-3 | ||
| 174 | +activation=linear | ||
| 175 | + | ||
| 176 | +[convolutional] | ||
| 177 | +xnor=1 | ||
| 178 | +batch_normalize=1 | ||
| 179 | +filters=128 | ||
| 180 | +size=1 | ||
| 181 | +stride=1 | ||
| 182 | +pad=1 | ||
| 183 | +activation=leaky | ||
| 184 | + | ||
| 185 | +[convolutional] | ||
| 186 | +xnor=1 | ||
| 187 | +batch_normalize=1 | ||
| 188 | +filters=256 | ||
| 189 | +size=3 | ||
| 190 | +stride=1 | ||
| 191 | +pad=1 | ||
| 192 | +activation=leaky | ||
| 193 | + | ||
| 194 | +[shortcut] | ||
| 195 | +from=-3 | ||
| 196 | +activation=linear | ||
| 197 | + | ||
| 198 | +[convolutional] | ||
| 199 | +xnor=1 | ||
| 200 | +batch_normalize=1 | ||
| 201 | +filters=128 | ||
| 202 | +size=1 | ||
| 203 | +stride=1 | ||
| 204 | +pad=1 | ||
| 205 | +activation=leaky | ||
| 206 | + | ||
| 207 | +[convolutional] | ||
| 208 | +xnor=1 | ||
| 209 | +batch_normalize=1 | ||
| 210 | +filters=256 | ||
| 211 | +size=3 | ||
| 212 | +stride=1 | ||
| 213 | +pad=1 | ||
| 214 | +activation=leaky | ||
| 215 | + | ||
| 216 | +[shortcut] | ||
| 217 | +from=-3 | ||
| 218 | +activation=linear | ||
| 219 | + | ||
| 220 | + | ||
| 221 | +[convolutional] | ||
| 222 | +xnor=1 | ||
| 223 | +batch_normalize=1 | ||
| 224 | +filters=128 | ||
| 225 | +size=1 | ||
| 226 | +stride=1 | ||
| 227 | +pad=1 | ||
| 228 | +activation=leaky | ||
| 229 | + | ||
| 230 | +[convolutional] | ||
| 231 | +xnor=1 | ||
| 232 | +batch_normalize=1 | ||
| 233 | +filters=256 | ||
| 234 | +size=3 | ||
| 235 | +stride=1 | ||
| 236 | +pad=1 | ||
| 237 | +activation=leaky | ||
| 238 | + | ||
| 239 | +[shortcut] | ||
| 240 | +from=-3 | ||
| 241 | +activation=linear | ||
| 242 | + | ||
| 243 | +[convolutional] | ||
| 244 | +xnor=1 | ||
| 245 | +batch_normalize=1 | ||
| 246 | +filters=128 | ||
| 247 | +size=1 | ||
| 248 | +stride=1 | ||
| 249 | +pad=1 | ||
| 250 | +activation=leaky | ||
| 251 | + | ||
| 252 | +[convolutional] | ||
| 253 | +xnor=1 | ||
| 254 | +batch_normalize=1 | ||
| 255 | +filters=256 | ||
| 256 | +size=3 | ||
| 257 | +stride=1 | ||
| 258 | +pad=1 | ||
| 259 | +activation=leaky | ||
| 260 | + | ||
| 261 | +[shortcut] | ||
| 262 | +from=-3 | ||
| 263 | +activation=linear | ||
| 264 | + | ||
| 265 | +[convolutional] | ||
| 266 | +xnor=1 | ||
| 267 | +batch_normalize=1 | ||
| 268 | +filters=128 | ||
| 269 | +size=1 | ||
| 270 | +stride=1 | ||
| 271 | +pad=1 | ||
| 272 | +activation=leaky | ||
| 273 | + | ||
| 274 | +[convolutional] | ||
| 275 | +xnor=1 | ||
| 276 | +batch_normalize=1 | ||
| 277 | +filters=256 | ||
| 278 | +size=3 | ||
| 279 | +stride=1 | ||
| 280 | +pad=1 | ||
| 281 | +activation=leaky | ||
| 282 | + | ||
| 283 | +[shortcut] | ||
| 284 | +from=-3 | ||
| 285 | +activation=linear | ||
| 286 | + | ||
| 287 | +[convolutional] | ||
| 288 | +xnor=1 | ||
| 289 | +batch_normalize=1 | ||
| 290 | +filters=128 | ||
| 291 | +size=1 | ||
| 292 | +stride=1 | ||
| 293 | +pad=1 | ||
| 294 | +activation=leaky | ||
| 295 | + | ||
| 296 | +[convolutional] | ||
| 297 | +xnor=1 | ||
| 298 | +batch_normalize=1 | ||
| 299 | +filters=256 | ||
| 300 | +size=3 | ||
| 301 | +stride=1 | ||
| 302 | +pad=1 | ||
| 303 | +activation=leaky | ||
| 304 | + | ||
| 305 | +[shortcut] | ||
| 306 | +from=-3 | ||
| 307 | +activation=linear | ||
| 308 | + | ||
| 309 | +# Downsample | ||
| 310 | + | ||
| 311 | +[convolutional] | ||
| 312 | +xnor=1 | ||
| 313 | +batch_normalize=1 | ||
| 314 | +filters=512 | ||
| 315 | +size=3 | ||
| 316 | +stride=2 | ||
| 317 | +pad=1 | ||
| 318 | +activation=leaky | ||
| 319 | + | ||
| 320 | +[convolutional] | ||
| 321 | +xnor=1 | ||
| 322 | +batch_normalize=1 | ||
| 323 | +filters=256 | ||
| 324 | +size=1 | ||
| 325 | +stride=1 | ||
| 326 | +pad=1 | ||
| 327 | +activation=leaky | ||
| 328 | + | ||
| 329 | +[convolutional] | ||
| 330 | +xnor=1 | ||
| 331 | +batch_normalize=1 | ||
| 332 | +filters=512 | ||
| 333 | +size=3 | ||
| 334 | +stride=1 | ||
| 335 | +pad=1 | ||
| 336 | +activation=leaky | ||
| 337 | + | ||
| 338 | +[shortcut] | ||
| 339 | +from=-3 | ||
| 340 | +activation=linear | ||
| 341 | + | ||
| 342 | + | ||
| 343 | +[convolutional] | ||
| 344 | +xnor=1 | ||
| 345 | +batch_normalize=1 | ||
| 346 | +filters=256 | ||
| 347 | +size=1 | ||
| 348 | +stride=1 | ||
| 349 | +pad=1 | ||
| 350 | +activation=leaky | ||
| 351 | + | ||
| 352 | +[convolutional] | ||
| 353 | +xnor=1 | ||
| 354 | +batch_normalize=1 | ||
| 355 | +filters=512 | ||
| 356 | +size=3 | ||
| 357 | +stride=1 | ||
| 358 | +pad=1 | ||
| 359 | +activation=leaky | ||
| 360 | + | ||
| 361 | +[shortcut] | ||
| 362 | +from=-3 | ||
| 363 | +activation=linear | ||
| 364 | + | ||
| 365 | + | ||
| 366 | +[convolutional] | ||
| 367 | +xnor=1 | ||
| 368 | +batch_normalize=1 | ||
| 369 | +filters=256 | ||
| 370 | +size=1 | ||
| 371 | +stride=1 | ||
| 372 | +pad=1 | ||
| 373 | +activation=leaky | ||
| 374 | + | ||
| 375 | +[convolutional] | ||
| 376 | +xnor=1 | ||
| 377 | +batch_normalize=1 | ||
| 378 | +filters=512 | ||
| 379 | +size=3 | ||
| 380 | +stride=1 | ||
| 381 | +pad=1 | ||
| 382 | +activation=leaky | ||
| 383 | + | ||
| 384 | +[shortcut] | ||
| 385 | +from=-3 | ||
| 386 | +activation=linear | ||
| 387 | + | ||
| 388 | + | ||
| 389 | +[convolutional] | ||
| 390 | +xnor=1 | ||
| 391 | +batch_normalize=1 | ||
| 392 | +filters=256 | ||
| 393 | +size=1 | ||
| 394 | +stride=1 | ||
| 395 | +pad=1 | ||
| 396 | +activation=leaky | ||
| 397 | + | ||
| 398 | +[convolutional] | ||
| 399 | +xnor=1 | ||
| 400 | +batch_normalize=1 | ||
| 401 | +filters=512 | ||
| 402 | +size=3 | ||
| 403 | +stride=1 | ||
| 404 | +pad=1 | ||
| 405 | +activation=leaky | ||
| 406 | + | ||
| 407 | +[shortcut] | ||
| 408 | +from=-3 | ||
| 409 | +activation=linear | ||
| 410 | + | ||
| 411 | +[convolutional] | ||
| 412 | +xnor=1 | ||
| 413 | +batch_normalize=1 | ||
| 414 | +filters=256 | ||
| 415 | +size=1 | ||
| 416 | +stride=1 | ||
| 417 | +pad=1 | ||
| 418 | +activation=leaky | ||
| 419 | + | ||
| 420 | +[convolutional] | ||
| 421 | +xnor=1 | ||
| 422 | +batch_normalize=1 | ||
| 423 | +filters=512 | ||
| 424 | +size=3 | ||
| 425 | +stride=1 | ||
| 426 | +pad=1 | ||
| 427 | +activation=leaky | ||
| 428 | + | ||
| 429 | +[shortcut] | ||
| 430 | +from=-3 | ||
| 431 | +activation=linear | ||
| 432 | + | ||
| 433 | + | ||
| 434 | +[convolutional] | ||
| 435 | +xnor=1 | ||
| 436 | +batch_normalize=1 | ||
| 437 | +filters=256 | ||
| 438 | +size=1 | ||
| 439 | +stride=1 | ||
| 440 | +pad=1 | ||
| 441 | +activation=leaky | ||
| 442 | + | ||
| 443 | +[convolutional] | ||
| 444 | +xnor=1 | ||
| 445 | +batch_normalize=1 | ||
| 446 | +filters=512 | ||
| 447 | +size=3 | ||
| 448 | +stride=1 | ||
| 449 | +pad=1 | ||
| 450 | +activation=leaky | ||
| 451 | + | ||
| 452 | +[shortcut] | ||
| 453 | +from=-3 | ||
| 454 | +activation=linear | ||
| 455 | + | ||
| 456 | + | ||
| 457 | +[convolutional] | ||
| 458 | +xnor=1 | ||
| 459 | +batch_normalize=1 | ||
| 460 | +filters=256 | ||
| 461 | +size=1 | ||
| 462 | +stride=1 | ||
| 463 | +pad=1 | ||
| 464 | +activation=leaky | ||
| 465 | + | ||
| 466 | +[convolutional] | ||
| 467 | +xnor=1 | ||
| 468 | +batch_normalize=1 | ||
| 469 | +filters=512 | ||
| 470 | +size=3 | ||
| 471 | +stride=1 | ||
| 472 | +pad=1 | ||
| 473 | +activation=leaky | ||
| 474 | + | ||
| 475 | +[shortcut] | ||
| 476 | +from=-3 | ||
| 477 | +activation=linear | ||
| 478 | + | ||
| 479 | +[convolutional] | ||
| 480 | +xnor=1 | ||
| 481 | +batch_normalize=1 | ||
| 482 | +filters=256 | ||
| 483 | +size=1 | ||
| 484 | +stride=1 | ||
| 485 | +pad=1 | ||
| 486 | +activation=leaky | ||
| 487 | + | ||
| 488 | +[convolutional] | ||
| 489 | +xnor=1 | ||
| 490 | +batch_normalize=1 | ||
| 491 | +filters=512 | ||
| 492 | +size=3 | ||
| 493 | +stride=1 | ||
| 494 | +pad=1 | ||
| 495 | +activation=leaky | ||
| 496 | + | ||
| 497 | +[shortcut] | ||
| 498 | +from=-3 | ||
| 499 | +activation=linear | ||
| 500 | + | ||
| 501 | +# Downsample | ||
| 502 | + | ||
| 503 | +[convolutional] | ||
| 504 | +xnor=1 | ||
| 505 | +batch_normalize=1 | ||
| 506 | +filters=1024 | ||
| 507 | +size=3 | ||
| 508 | +stride=2 | ||
| 509 | +pad=1 | ||
| 510 | +activation=leaky | ||
| 511 | + | ||
| 512 | +[convolutional] | ||
| 513 | +xnor=1 | ||
| 514 | +batch_normalize=1 | ||
| 515 | +filters=512 | ||
| 516 | +size=1 | ||
| 517 | +stride=1 | ||
| 518 | +pad=1 | ||
| 519 | +activation=leaky | ||
| 520 | + | ||
| 521 | +[convolutional] | ||
| 522 | +xnor=1 | ||
| 523 | +batch_normalize=1 | ||
| 524 | +filters=1024 | ||
| 525 | +size=3 | ||
| 526 | +stride=1 | ||
| 527 | +pad=1 | ||
| 528 | +activation=leaky | ||
| 529 | + | ||
| 530 | +[shortcut] | ||
| 531 | +from=-3 | ||
| 532 | +activation=linear | ||
| 533 | + | ||
| 534 | +[convolutional] | ||
| 535 | +xnor=1 | ||
| 536 | +batch_normalize=1 | ||
| 537 | +filters=512 | ||
| 538 | +size=1 | ||
| 539 | +stride=1 | ||
| 540 | +pad=1 | ||
| 541 | +activation=leaky | ||
| 542 | + | ||
| 543 | +[convolutional] | ||
| 544 | +xnor=1 | ||
| 545 | +batch_normalize=1 | ||
| 546 | +filters=1024 | ||
| 547 | +size=3 | ||
| 548 | +stride=1 | ||
| 549 | +pad=1 | ||
| 550 | +activation=leaky | ||
| 551 | + | ||
| 552 | +[shortcut] | ||
| 553 | +from=-3 | ||
| 554 | +activation=linear | ||
| 555 | + | ||
| 556 | +[convolutional] | ||
| 557 | +xnor=1 | ||
| 558 | +batch_normalize=1 | ||
| 559 | +filters=512 | ||
| 560 | +size=1 | ||
| 561 | +stride=1 | ||
| 562 | +pad=1 | ||
| 563 | +activation=leaky | ||
| 564 | + | ||
| 565 | +[convolutional] | ||
| 566 | +xnor=1 | ||
| 567 | +batch_normalize=1 | ||
| 568 | +filters=1024 | ||
| 569 | +size=3 | ||
| 570 | +stride=1 | ||
| 571 | +pad=1 | ||
| 572 | +activation=leaky | ||
| 573 | + | ||
| 574 | +[shortcut] | ||
| 575 | +from=-3 | ||
| 576 | +activation=linear | ||
| 577 | + | ||
| 578 | +[convolutional] | ||
| 579 | +xnor=1 | ||
| 580 | +batch_normalize=1 | ||
| 581 | +filters=512 | ||
| 582 | +size=1 | ||
| 583 | +stride=1 | ||
| 584 | +pad=1 | ||
| 585 | +activation=leaky | ||
| 586 | + | ||
| 587 | +[convolutional] | ||
| 588 | +xnor=1 | ||
| 589 | +batch_normalize=1 | ||
| 590 | +filters=1024 | ||
| 591 | +size=3 | ||
| 592 | +stride=1 | ||
| 593 | +pad=1 | ||
| 594 | +activation=leaky | ||
| 595 | + | ||
| 596 | +[shortcut] | ||
| 597 | +from=-3 | ||
| 598 | +activation=linear | ||
| 599 | + | ||
| 600 | +[convolutional] | ||
| 601 | +batch_normalize=1 | ||
| 602 | +filters=512 | ||
| 603 | +size=1 | ||
| 604 | +stride=1 | ||
| 605 | +pad=1 | ||
| 606 | +activation=leaky | ||
| 607 | + | ||
| 608 | +[avgpool] | ||
| 609 | + | ||
| 610 | +[convolutional] | ||
| 611 | +filters=1000 | ||
| 612 | +size=1 | ||
| 613 | +stride=1 | ||
| 614 | +pad=1 | ||
| 615 | +activation=linear | ||
| 616 | + | ||
| 617 | +[softmax] | ||
| 618 | +groups=1 | ||
| 619 | + |
| 1 | +[net] | ||
| 2 | +batch=128 | ||
| 3 | +subdivisions=1 | ||
| 4 | +height=224 | ||
| 5 | +width=224 | ||
| 6 | +max_crop=320 | ||
| 7 | +channels=3 | ||
| 8 | +momentum=0.9 | ||
| 9 | +decay=0.0005 | ||
| 10 | + | ||
| 11 | +learning_rate=0.1 | ||
| 12 | +policy=poly | ||
| 13 | +power=4 | ||
| 14 | +max_batches=1600000 | ||
| 15 | + | ||
| 16 | +[convolutional] | ||
| 17 | +batch_normalize=1 | ||
| 18 | +filters=64 | ||
| 19 | +size=7 | ||
| 20 | +stride=2 | ||
| 21 | +pad=1 | ||
| 22 | +activation=leaky | ||
| 23 | + | ||
| 24 | +[maxpool] | ||
| 25 | +size=2 | ||
| 26 | +stride=2 | ||
| 27 | + | ||
| 28 | +[convolutional] | ||
| 29 | +batch_normalize=1 | ||
| 30 | +filters=192 | ||
| 31 | +size=3 | ||
| 32 | +stride=1 | ||
| 33 | +pad=1 | ||
| 34 | +activation=leaky | ||
| 35 | + | ||
| 36 | +[maxpool] | ||
| 37 | +size=2 | ||
| 38 | +stride=2 | ||
| 39 | + | ||
| 40 | +[convolutional] | ||
| 41 | +batch_normalize=1 | ||
| 42 | +filters=128 | ||
| 43 | +size=1 | ||
| 44 | +stride=1 | ||
| 45 | +pad=1 | ||
| 46 | +activation=leaky | ||
| 47 | + | ||
| 48 | +[convolutional] | ||
| 49 | +batch_normalize=1 | ||
| 50 | +filters=256 | ||
| 51 | +size=3 | ||
| 52 | +stride=1 | ||
| 53 | +pad=1 | ||
| 54 | +activation=leaky | ||
| 55 | + | ||
| 56 | +[convolutional] | ||
| 57 | +batch_normalize=1 | ||
| 58 | +filters=256 | ||
| 59 | +size=1 | ||
| 60 | +stride=1 | ||
| 61 | +pad=1 | ||
| 62 | +activation=leaky | ||
| 63 | + | ||
| 64 | +[convolutional] | ||
| 65 | +batch_normalize=1 | ||
| 66 | +filters=512 | ||
| 67 | +size=3 | ||
| 68 | +stride=1 | ||
| 69 | +pad=1 | ||
| 70 | +activation=leaky | ||
| 71 | + | ||
| 72 | +[maxpool] | ||
| 73 | +size=2 | ||
| 74 | +stride=2 | ||
| 75 | + | ||
| 76 | +[convolutional] | ||
| 77 | +batch_normalize=1 | ||
| 78 | +filters=256 | ||
| 79 | +size=1 | ||
| 80 | +stride=1 | ||
| 81 | +pad=1 | ||
| 82 | +activation=leaky | ||
| 83 | + | ||
| 84 | +[convolutional] | ||
| 85 | +batch_normalize=1 | ||
| 86 | +filters=512 | ||
| 87 | +size=3 | ||
| 88 | +stride=1 | ||
| 89 | +pad=1 | ||
| 90 | +activation=leaky | ||
| 91 | + | ||
| 92 | +[convolutional] | ||
| 93 | +batch_normalize=1 | ||
| 94 | +filters=256 | ||
| 95 | +size=1 | ||
| 96 | +stride=1 | ||
| 97 | +pad=1 | ||
| 98 | +activation=leaky | ||
| 99 | + | ||
| 100 | +[convolutional] | ||
| 101 | +batch_normalize=1 | ||
| 102 | +filters=512 | ||
| 103 | +size=3 | ||
| 104 | +stride=1 | ||
| 105 | +pad=1 | ||
| 106 | +activation=leaky | ||
| 107 | + | ||
| 108 | +[convolutional] | ||
| 109 | +batch_normalize=1 | ||
| 110 | +filters=256 | ||
| 111 | +size=1 | ||
| 112 | +stride=1 | ||
| 113 | +pad=1 | ||
| 114 | +activation=leaky | ||
| 115 | + | ||
| 116 | +[convolutional] | ||
| 117 | +batch_normalize=1 | ||
| 118 | +filters=512 | ||
| 119 | +size=3 | ||
| 120 | +stride=1 | ||
| 121 | +pad=1 | ||
| 122 | +activation=leaky | ||
| 123 | + | ||
| 124 | +[convolutional] | ||
| 125 | +batch_normalize=1 | ||
| 126 | +filters=256 | ||
| 127 | +size=1 | ||
| 128 | +stride=1 | ||
| 129 | +pad=1 | ||
| 130 | +activation=leaky | ||
| 131 | + | ||
| 132 | +[convolutional] | ||
| 133 | +batch_normalize=1 | ||
| 134 | +filters=512 | ||
| 135 | +size=3 | ||
| 136 | +stride=1 | ||
| 137 | +pad=1 | ||
| 138 | +activation=leaky | ||
| 139 | + | ||
| 140 | +[convolutional] | ||
| 141 | +batch_normalize=1 | ||
| 142 | +filters=512 | ||
| 143 | +size=1 | ||
| 144 | +stride=1 | ||
| 145 | +pad=1 | ||
| 146 | +activation=leaky | ||
| 147 | + | ||
| 148 | +[convolutional] | ||
| 149 | +batch_normalize=1 | ||
| 150 | +filters=1024 | ||
| 151 | +size=3 | ||
| 152 | +stride=1 | ||
| 153 | +pad=1 | ||
| 154 | +activation=leaky | ||
| 155 | + | ||
| 156 | +[maxpool] | ||
| 157 | +size=2 | ||
| 158 | +stride=2 | ||
| 159 | + | ||
| 160 | +[convolutional] | ||
| 161 | +batch_normalize=1 | ||
| 162 | +filters=512 | ||
| 163 | +size=1 | ||
| 164 | +stride=1 | ||
| 165 | +pad=1 | ||
| 166 | +activation=leaky | ||
| 167 | + | ||
| 168 | +[convolutional] | ||
| 169 | +batch_normalize=1 | ||
| 170 | +filters=1024 | ||
| 171 | +size=3 | ||
| 172 | +stride=1 | ||
| 173 | +pad=1 | ||
| 174 | +activation=leaky | ||
| 175 | + | ||
| 176 | +[convolutional] | ||
| 177 | +batch_normalize=1 | ||
| 178 | +filters=512 | ||
| 179 | +size=1 | ||
| 180 | +stride=1 | ||
| 181 | +pad=1 | ||
| 182 | +activation=leaky | ||
| 183 | + | ||
| 184 | +[convolutional] | ||
| 185 | +batch_normalize=1 | ||
| 186 | +filters=1024 | ||
| 187 | +size=3 | ||
| 188 | +stride=1 | ||
| 189 | +pad=1 | ||
| 190 | +activation=leaky | ||
| 191 | + | ||
| 192 | +[convolutional] | ||
| 193 | +filters=1000 | ||
| 194 | +size=1 | ||
| 195 | +stride=1 | ||
| 196 | +pad=1 | ||
| 197 | +activation=leaky | ||
| 198 | + | ||
| 199 | +[avgpool] | ||
| 200 | + | ||
| 201 | +[softmax] | ||
| 202 | +groups=1 | ||
| 203 | + | ||
| 204 | +[cost] | ||
| 205 | +type=sse | ||
| 206 | + |
| 1 | +[net] | ||
| 2 | +batch=1 | ||
| 3 | +subdivisions=1 | ||
| 4 | +height=256 | ||
| 5 | +width=256 | ||
| 6 | +channels=3 | ||
| 7 | +momentum=0.9 | ||
| 8 | +decay=0.0005 | ||
| 9 | + | ||
| 10 | +learning_rate=0.5 | ||
| 11 | +policy=poly | ||
| 12 | +power=6 | ||
| 13 | +max_batches=500000 | ||
| 14 | + | ||
| 15 | +[convolutional] | ||
| 16 | +filters=64 | ||
| 17 | +size=7 | ||
| 18 | +stride=2 | ||
| 19 | +pad=1 | ||
| 20 | +activation=leaky | ||
| 21 | + | ||
| 22 | +[maxpool] | ||
| 23 | +size=2 | ||
| 24 | +stride=2 | ||
| 25 | + | ||
| 26 | +[convolutional] | ||
| 27 | +filters=192 | ||
| 28 | +size=3 | ||
| 29 | +stride=1 | ||
| 30 | +pad=1 | ||
| 31 | +activation=leaky | ||
| 32 | + | ||
| 33 | +[maxpool] | ||
| 34 | +size=2 | ||
| 35 | +stride=2 | ||
| 36 | + | ||
| 37 | +[convolutional] | ||
| 38 | +filters=128 | ||
| 39 | +size=1 | ||
| 40 | +stride=1 | ||
| 41 | +pad=1 | ||
| 42 | +activation=leaky | ||
| 43 | + | ||
| 44 | +[convolutional] | ||
| 45 | +filters=256 | ||
| 46 | +size=3 | ||
| 47 | +stride=1 | ||
| 48 | +pad=1 | ||
| 49 | +activation=leaky | ||
| 50 | + | ||
| 51 | +[convolutional] | ||
| 52 | +filters=256 | ||
| 53 | +size=1 | ||
| 54 | +stride=1 | ||
| 55 | +pad=1 | ||
| 56 | +activation=leaky | ||
| 57 | + | ||
| 58 | +[convolutional] | ||
| 59 | +filters=512 | ||
| 60 | +size=3 | ||
| 61 | +stride=1 | ||
| 62 | +pad=1 | ||
| 63 | +activation=leaky | ||
| 64 | + | ||
| 65 | +[maxpool] | ||
| 66 | +size=2 | ||
| 67 | +stride=2 | ||
| 68 | + | ||
| 69 | +[convolutional] | ||
| 70 | +filters=256 | ||
| 71 | +size=1 | ||
| 72 | +stride=1 | ||
| 73 | +pad=1 | ||
| 74 | +activation=leaky | ||
| 75 | + | ||
| 76 | +[convolutional] | ||
| 77 | +filters=512 | ||
| 78 | +size=3 | ||
| 79 | +stride=1 | ||
| 80 | +pad=1 | ||
| 81 | +activation=leaky | ||
| 82 | + | ||
| 83 | +[convolutional] | ||
| 84 | +filters=256 | ||
| 85 | +size=1 | ||
| 86 | +stride=1 | ||
| 87 | +pad=1 | ||
| 88 | +activation=leaky | ||
| 89 | + | ||
| 90 | +[convolutional] | ||
| 91 | +filters=512 | ||
| 92 | +size=3 | ||
| 93 | +stride=1 | ||
| 94 | +pad=1 | ||
| 95 | +activation=leaky | ||
| 96 | + | ||
| 97 | +[convolutional] | ||
| 98 | +filters=256 | ||
| 99 | +size=1 | ||
| 100 | +stride=1 | ||
| 101 | +pad=1 | ||
| 102 | +activation=leaky | ||
| 103 | + | ||
| 104 | +[convolutional] | ||
| 105 | +filters=512 | ||
| 106 | +size=3 | ||
| 107 | +stride=1 | ||
| 108 | +pad=1 | ||
| 109 | +activation=leaky | ||
| 110 | + | ||
| 111 | +[convolutional] | ||
| 112 | +filters=256 | ||
| 113 | +size=1 | ||
| 114 | +stride=1 | ||
| 115 | +pad=1 | ||
| 116 | +activation=leaky | ||
| 117 | + | ||
| 118 | +[convolutional] | ||
| 119 | +filters=512 | ||
| 120 | +size=3 | ||
| 121 | +stride=1 | ||
| 122 | +pad=1 | ||
| 123 | +activation=leaky | ||
| 124 | + | ||
| 125 | +[convolutional] | ||
| 126 | +filters=512 | ||
| 127 | +size=1 | ||
| 128 | +stride=1 | ||
| 129 | +pad=1 | ||
| 130 | +activation=leaky | ||
| 131 | + | ||
| 132 | +[convolutional] | ||
| 133 | +filters=1024 | ||
| 134 | +size=3 | ||
| 135 | +stride=1 | ||
| 136 | +pad=1 | ||
| 137 | +activation=leaky | ||
| 138 | + | ||
| 139 | +[maxpool] | ||
| 140 | +size=2 | ||
| 141 | +stride=2 | ||
| 142 | + | ||
| 143 | +[convolutional] | ||
| 144 | +filters=512 | ||
| 145 | +size=1 | ||
| 146 | +stride=1 | ||
| 147 | +pad=1 | ||
| 148 | +activation=leaky | ||
| 149 | + | ||
| 150 | +[convolutional] | ||
| 151 | +filters=1024 | ||
| 152 | +size=3 | ||
| 153 | +stride=1 | ||
| 154 | +pad=1 | ||
| 155 | +activation=leaky | ||
| 156 | + | ||
| 157 | +[convolutional] | ||
| 158 | +filters=512 | ||
| 159 | +size=1 | ||
| 160 | +stride=1 | ||
| 161 | +pad=1 | ||
| 162 | +activation=leaky | ||
| 163 | + | ||
| 164 | +[convolutional] | ||
| 165 | +filters=1024 | ||
| 166 | +size=3 | ||
| 167 | +stride=1 | ||
| 168 | +pad=1 | ||
| 169 | +activation=leaky | ||
| 170 | + | ||
| 171 | +[avgpool] | ||
| 172 | + | ||
| 173 | +[connected] | ||
| 174 | +output=1000 | ||
| 175 | +activation=leaky | ||
| 176 | + | ||
| 177 | +[softmax] | ||
| 178 | +groups=1 | ||
| 179 | + |
| 1 | +[net] | ||
| 2 | +batch=128 | ||
| 3 | +subdivisions=1 | ||
| 4 | +height=224 | ||
| 5 | +width=224 | ||
| 6 | +max_crop=320 | ||
| 7 | +channels=3 | ||
| 8 | +momentum=0.9 | ||
| 9 | +decay=0.0005 | ||
| 10 | + | ||
| 11 | +learning_rate=0.01 | ||
| 12 | +max_batches = 0 | ||
| 13 | +policy=steps | ||
| 14 | +steps=444000,590000,970000 | ||
| 15 | +scales=.5,.2,.1 | ||
| 16 | + | ||
| 17 | +#policy=sigmoid | ||
| 18 | +#gamma=.00008 | ||
| 19 | +#step=100000 | ||
| 20 | +#max_batches=200000 | ||
| 21 | + | ||
| 22 | +[convolutional] | ||
| 23 | +batch_normalize=1 | ||
| 24 | +filters=64 | ||
| 25 | +size=7 | ||
| 26 | +stride=2 | ||
| 27 | +pad=1 | ||
| 28 | +activation=leaky | ||
| 29 | + | ||
| 30 | +[maxpool] | ||
| 31 | +size=2 | ||
| 32 | +stride=2 | ||
| 33 | + | ||
| 34 | +[convolutional] | ||
| 35 | +batch_normalize=1 | ||
| 36 | +filters=192 | ||
| 37 | +size=3 | ||
| 38 | +stride=1 | ||
| 39 | +pad=1 | ||
| 40 | +activation=leaky | ||
| 41 | + | ||
| 42 | +[maxpool] | ||
| 43 | +size=2 | ||
| 44 | +stride=2 | ||
| 45 | + | ||
| 46 | +[convolutional] | ||
| 47 | +batch_normalize=1 | ||
| 48 | +filters=128 | ||
| 49 | +size=1 | ||
| 50 | +stride=1 | ||
| 51 | +pad=1 | ||
| 52 | +activation=leaky | ||
| 53 | + | ||
| 54 | +[convolutional] | ||
| 55 | +batch_normalize=1 | ||
| 56 | +filters=256 | ||
| 57 | +size=3 | ||
| 58 | +stride=1 | ||
| 59 | +pad=1 | ||
| 60 | +activation=leaky | ||
| 61 | + | ||
| 62 | +[convolutional] | ||
| 63 | +batch_normalize=1 | ||
| 64 | +filters=256 | ||
| 65 | +size=1 | ||
| 66 | +stride=1 | ||
| 67 | +pad=1 | ||
| 68 | +activation=leaky | ||
| 69 | + | ||
| 70 | +[convolutional] | ||
| 71 | +batch_normalize=1 | ||
| 72 | +filters=512 | ||
| 73 | +size=3 | ||
| 74 | +stride=1 | ||
| 75 | +pad=1 | ||
| 76 | +activation=leaky | ||
| 77 | + | ||
| 78 | +[maxpool] | ||
| 79 | +size=2 | ||
| 80 | +stride=2 | ||
| 81 | + | ||
| 82 | +[convolutional] | ||
| 83 | +batch_normalize=1 | ||
| 84 | +filters=256 | ||
| 85 | +size=1 | ||
| 86 | +stride=1 | ||
| 87 | +pad=1 | ||
| 88 | +activation=leaky | ||
| 89 | + | ||
| 90 | +[convolutional] | ||
| 91 | +batch_normalize=1 | ||
| 92 | +filters=512 | ||
| 93 | +size=3 | ||
| 94 | +stride=1 | ||
| 95 | +pad=1 | ||
| 96 | +activation=leaky | ||
| 97 | + | ||
| 98 | +[convolutional] | ||
| 99 | +batch_normalize=1 | ||
| 100 | +filters=256 | ||
| 101 | +size=1 | ||
| 102 | +stride=1 | ||
| 103 | +pad=1 | ||
| 104 | +activation=leaky | ||
| 105 | + | ||
| 106 | +[convolutional] | ||
| 107 | +batch_normalize=1 | ||
| 108 | +filters=512 | ||
| 109 | +size=3 | ||
| 110 | +stride=1 | ||
| 111 | +pad=1 | ||
| 112 | +activation=leaky | ||
| 113 | + | ||
| 114 | +[convolutional] | ||
| 115 | +batch_normalize=1 | ||
| 116 | +filters=256 | ||
| 117 | +size=1 | ||
| 118 | +stride=1 | ||
| 119 | +pad=1 | ||
| 120 | +activation=leaky | ||
| 121 | + | ||
| 122 | +[convolutional] | ||
| 123 | +batch_normalize=1 | ||
| 124 | +filters=512 | ||
| 125 | +size=3 | ||
| 126 | +stride=1 | ||
| 127 | +pad=1 | ||
| 128 | +activation=leaky | ||
| 129 | + | ||
| 130 | +[convolutional] | ||
| 131 | +batch_normalize=1 | ||
| 132 | +filters=256 | ||
| 133 | +size=1 | ||
| 134 | +stride=1 | ||
| 135 | +pad=1 | ||
| 136 | +activation=leaky | ||
| 137 | + | ||
| 138 | +[convolutional] | ||
| 139 | +batch_normalize=1 | ||
| 140 | +filters=512 | ||
| 141 | +size=3 | ||
| 142 | +stride=1 | ||
| 143 | +pad=1 | ||
| 144 | +activation=leaky | ||
| 145 | + | ||
| 146 | +[convolutional] | ||
| 147 | +batch_normalize=1 | ||
| 148 | +filters=512 | ||
| 149 | +size=1 | ||
| 150 | +stride=1 | ||
| 151 | +pad=1 | ||
| 152 | +activation=leaky | ||
| 153 | + | ||
| 154 | +[convolutional] | ||
| 155 | +batch_normalize=1 | ||
| 156 | +filters=1024 | ||
| 157 | +size=3 | ||
| 158 | +stride=1 | ||
| 159 | +pad=1 | ||
| 160 | +activation=leaky | ||
| 161 | + | ||
| 162 | +[maxpool] | ||
| 163 | +size=2 | ||
| 164 | +stride=2 | ||
| 165 | + | ||
| 166 | +[convolutional] | ||
| 167 | +batch_normalize=1 | ||
| 168 | +filters=1024 | ||
| 169 | +size=1 | ||
| 170 | +stride=1 | ||
| 171 | +pad=1 | ||
| 172 | +activation=leaky | ||
| 173 | + | ||
| 174 | +[convolutional] | ||
| 175 | +batch_normalize=1 | ||
| 176 | +filters=2048 | ||
| 177 | +size=3 | ||
| 178 | +stride=1 | ||
| 179 | +pad=1 | ||
| 180 | +activation=leaky | ||
| 181 | + | ||
| 182 | +[convolutional] | ||
| 183 | +batch_normalize=1 | ||
| 184 | +filters=1024 | ||
| 185 | +size=1 | ||
| 186 | +stride=1 | ||
| 187 | +pad=1 | ||
| 188 | +activation=leaky | ||
| 189 | + | ||
| 190 | +[convolutional] | ||
| 191 | +batch_normalize=1 | ||
| 192 | +filters=2048 | ||
| 193 | +size=3 | ||
| 194 | +stride=1 | ||
| 195 | +pad=1 | ||
| 196 | +activation=leaky | ||
| 197 | + | ||
| 198 | +[avgpool] | ||
| 199 | + | ||
| 200 | +[connected] | ||
| 201 | +output=21842 | ||
| 202 | +activation=leaky | ||
| 203 | + | ||
| 204 | +[softmax] | ||
| 205 | +groups=1 | ||
| 206 | + | ||
| 207 | +[cost] | ||
| 208 | +type=sse | ||
| 209 | + |
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