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+#!/usr/bin/env sh
+
+# DEFINE
+DATA_ROOT_PATH=""
+DEVICE="cuda"
+
+############################## Table 3 #########################################
+#### This commands Assumes that you have computed the best models for the architecture
+#### or table 1 commands are already run
+#### We show commands for k=2,
+#### for other values of k just modify the frame_keep_fraction parameter
+#### k=2  -> 0.5
+#### k=4  -> 0.25
+#### k=5  -> 0.2
+#### k=10 -> 0.1
+################################################################################
+
+# 3d cnn with 50% frames (k=2), with imputation
+python3 -m src.scripts.main -c config/config.py \
+  --exp_name 3d_cnn_eval_k=2_imputed \
+  -r /tmp \
+  --mode test \
+  --device $DEVICE --wandb.use 0 \
+  --model.arch.file src/arch/brain_age_3d.py \
+  --data.root_path "$DATA_ROOT_PATH" \
+  --data.frame_keep_style random --data.frame_keep_fraction 0.5 \
+  --data.impute fill \
+  --statefile result/3d_cnn/run_0001/best_model.pt
+
+# 2d lstm with 50% frames (k=2), without imputation
+python3 -m src.scripts.main -c config/config.py \
+  --exp_name 2d_slice_lstm_eval_k=2 \
+  -r /tmp \
+  --mode test \
+  --device $DEVICE --wandb.use 0 \
+  --model.arch.file src/arch/brain_age_slice_lstm.py \
+  --data.root_path "$DATA_ROOT_PATH" \
+  --data.frame_keep_style random --data.frame_keep_fraction 0.5 \
+  --data.impute drop \
+  --statefile result/2d_slice_lstm/run_0001/best_model.pt
+
+# 2d lstm with 50% frames (k=2), with imputation
+python3 -m src.scripts.main -c config/config.py \
+  --exp_name 2d_slice_lstm_eval_k=2_imputed \
+  -r /tmp \
+  --mode test \
+  --device $DEVICE --wandb.use 0 \
+  --model.arch.file src/arch/brain_age_slice_lstm.py \
+  --data.root_path "$DATA_ROOT_PATH" \
+  --data.frame_keep_style random --data.frame_keep_fraction 0.5 \
+  --data.impute fill \
+  --statefile result/2d_slice_lstm/run_0001/best_model.pt
+
+# 2D-slice-attention with 50% frames (k=2), without imputation
+python3 -m src.scripts.main -c config/config.py \
+  --exp_name 2d_slice_attention_eval_k=2 \
+  -r /tmp \
+  --mode test \
+  --device $DEVICE --wandb.use 0 \
+  --model.arch.file src/arch/brain_age_slice_set.py \
+  --model.arch.attn_dim 32 --model.arch.attn_num_heads 1 \
+  --model.arch.attn_drop 1 --model.arch.agg_fn "attention" \
+  --data.root_path "$DATA_ROOT_PATH" \
+  --data.frame_keep_style random --data.frame_keep_fraction 0.5 \
+  --data.impute drop \
+  --statefile result/2d_slice_attention/run_0001/best_model.pt
+
+# 2D-slice-mean with 50% frames (k=2), without imputation
+python3 -m src.scripts.main -c config/config.py \
+  --exp_name 2d_slice_mean_eval_k=2 \
+  -r /tmp \
+  --mode test \
+  --device $DEVICE --wandb.use 0 \
+  --model.arch.file src/arch/brain_age_slice_set.py \
+  --model.arch.attn_dim 32 --model.arch.attn_num_heads 1 \
+  --model.arch.attn_drop 1 --model.arch.agg_fn "mean" \
+  --data.root_path "$DATA_ROOT_PATH" \
+  --data.frame_keep_style random --data.frame_keep_fraction 0.5 \
+  --data.impute drop \
+  --statefile result/2d_slice_attention/run_0001/best_model.pt
+
+# 2D-slice-max with 50% frames (k=2), without imputation
+python3 -m src.scripts.main -c config/config.py \
+  --exp_name 2d_slice_max_eval_k=2 \
+  -r /tmp \
+  --mode test \
+  --device $DEVICE --wandb.use 0 \
+  --model.arch.file src/arch/brain_age_slice_set.py \
+  --model.arch.attn_dim 32 --model.arch.attn_num_heads 1 \
+  --model.arch.attn_drop 1 --model.arch.agg_fn "max" \
+  --data.root_path "$DATA_ROOT_PATH" \
+  --data.frame_keep_style random --data.frame_keep_fraction 0.5 \
+  --data.impute drop \
+  --statefile result/2d_slice_attention/run_0001/best_model.pt