Baseline code for [TinyAction Challenge] [Paper] [Dataset]
-Prerequisites: Python 3.6, NumPy, SciPy, OpenCV, PyTorch (>=1.6), torchvision
Download the dataset and extract the files. Once done, change the dataset path in configuration.py file accordingly.
(Optional) Download the pretrained weights for the baseline models [I3D] [R(2+1)D] [WideResnet].
We provide training setup for I3D, R3D, R2+1D and wideresnet models. If you wish to use pretrained weights, change the path for --pretrain_path. The other options are related to each model (from their papers). Use the following command for each model:
python main.py --result_path results --sub_path i3d --model i3d --model_depth 18 --n_classes 26 --n_pretrain_classes 157 --pretrain_path weights/rgb_charades.pt --num_frames 32 --skip_frames 1 --sample_size 224 --learning_rate 0.001 --optimizer adam --batch_size 24 --n_threads 4 --checkpoint 5
python main.py --result_path results --sub_path r3d_18 --model resnet --model_depth 18 --n_classes 26 --n_pretrain_classes 700 --pretrain_path weights/r3d18_K_200ep.pth --num_frames 16 --skip_frames 1 --sample_size 112 --learning_rate 0.001 --optimizer adam --batch_size 96 --n_threads 4 --checkpoint 5
python main.py --result_path results --sub_path r2p1d50 --model resnet2p1d --model_depth 50 --n_classes 26 --n_pretrain_classes 700 --pretrain_path weights/r2p1d50_K_200ep.pth --num_frames 16 --skip_frames 1 --sample_size 112 --learning_rate 0.001 --optimizer adam --batch_size 64 --n_threads 4 --checkpoint 5
python main.py --result_path results --sub_path wideresnet --model wideresnet --model_depth 50 --n_classes 26 --n_pretrain_classes 400 --pretrain_path weights/wideresnet-50-kinetics.pth --num_frames 16 --skip_frames 1 --sample_size 112 --learning_rate 0.001 --optimizer adam --batch_size 64 --n_threads 4 --checkpoint 5 --resnet_shortcut B
To prepare the evaluation file for submission, use the following after changing --pretrain_path with your trained model:
python evaluate_variants.py --model i3d --model_depth 18 --n_classes 26 --n_pretrain_classes 26 --pretrain_path results/i3d/save.pth --num_frames 32 --skip_frames 1 --sample_size 224 --n_threads 4
python evaluate.py --sub_path r3d_18 --model resnet --model_depth 18 --n_classes 26 --n_pretrain_classes 26 --pretrain_path results/r3d/save.pth --num_frames 16 --skip_frames 1 --sample_size 112 --n_threads 4
python evaluate_variants.py --model resnet2p1d --model_depth 50 --n_classes 26 --n_pretrain_classes 26 --pretrain_path results/r2p1d50/save.pth --num_frames 16 --skip_frames 1 --sample_size 112 --n_threads 4
python evaluate_variants.py --model wideresnet --model_depth 50 --n_classes 26 --n_pretrain_classes 26 --pretrain_path results/wideresnet/save.pth --num_frames 16 --skip_frames 1 --sample_size 112 --n_threads 4 --resnet_shortcut B