TDN: Temporal Difference Networks for Efficient Action Recognition

Overview

We release the PyTorch code of the TDN(Temporal Difference Networks). This code is based on the TSN and TSM codebase. The core code to implement the Temporal Difference Module are ops/base_module.py and ops/tdn_net.py.

🔥 [NEW!] We have released the PyTorch code of TDN.

Prerequisites
Data Preparation
Model Zoo
Testing
Training

Prerequisites

The code is built with following libraries:

Python 3.6 or higher
PyTorch 1.4 or higher
Torchvision
TensorboardX
tqdm
scikit-learn
ffmpeg
decord

Data Preparation

We have successfully trained TDN on Kinetics400, UCF101, HMDB51, Something-Something-V1 and V2 with this codebase.

The processing of Something-Something-V1 & V2 can be summarized into 3 steps:
1. Extract frames from videos(you can use ffmpeg to get frames from video)
2. Generate annotations needed for dataloader ("
  " in annotations) The annotation usually includes train.txt and val.txt. The format of *.txt file is like:
  frames/video_1 num_frames label_1 frames/video_2 num_frames label_2 frames/video_3 num_frames label_3 ... frames/video_N num_frames label_N
3. Add the information to ops/dataset_configs.py
The processing of Kinetics400 can be summarized into 2 steps:
1. Generate annotations needed for dataloader ("
  " in annotations) The annotation usually includes train.txt and val.txt. The format of *.txt file is like:
```
frames/video_1.mp4  label_1
frames/video_2.mp4  label_2
frames/video_3.mp4  label_3
...
frames/video_N.mp4  label_N
```
2. Add the information to ops/dataset_configs.py

Model Zoo

Here we provide some off-the-shelf pretrained models. The accuracy might vary a little bit compared to the paper, since the raw video of Kinetics downloaded by users may have some differences.

Something-Something-V1

Model	Frames x Crops x Clips	Top-1	Top-5	checkpoint
TDN-ResNet50	8x1x1	52.3%	80.6%	link
TDN-ResNet50	16x1x1	53.9%	82.1%	link

Something-Something-V2

Model	Frames x Crops x Clips	Top-1	Top-5	checkpoint
TDN-ResNet50	8x1x1	64.0%	88.8%	link
TDN-ResNet50	16x1x1	65.3%	89.7%	link

Kinetics400

Model	Frames x Crops x Clips	Top-1 (30 view)	Top-5 (30 view)	checkpoint
TDN-ResNet50	8x3x10	76.6%	92.8%	link
TDN-ResNet50	16x3x10	77.5%	93.2%	link
TDN-ResNet101	8x3x10	77.5%	93.6%	link
TDN-ResNet101	16x3x10	78.5%	93.9%	link

Testing

For center crop single clip, the processing of testing can be summarized into 2 steps:

Run the following testing scripts:

CUDA_VISIBLE_DEVICES=0 python3 test_models_center_crop.py something \
--archs='resnet50' --weights   --test_segments=8  \
--test_crops=1 --batch_size=16  --gpus 0 --output_dir  -j 4 --clip_index=1

Run the following scripts to get result from the raw score:

python3 pkl_to_results.py --num_clips 1 --test_crops 1 --output_dir

For 3 crops, 10 clips, the processing of testing can be summarized into 2 steps:

Run the following testing scripts for 10 times(clip_index from 0 to 9):

CUDA_VISIBLE_DEVICES=0 python3 test_models_three_crops.py  kinetics \
--archs='resnet50' --weights   --test_segments=8 \
--test_crops=3 --batch_size=16 --full_res --gpus 0 --output_dir   \
-j 4 --clip_index

Run the following scripts to ensemble the raw score of the 30 views:

python pkl_to_results.py --num_clips 10 --test_crops 3 --output_dir

Training

This implementation supports multi-gpu, DistributedDataParallel training, which is faster and simpler.

For example, to train TDN-ResNet50 on Something-Something-V1 with 8 gpus, you can run:

python -m torch.distributed.launch --master_port 12347 --nproc_per_node=8 \
            main.py  something  RGB --arch resnet50 --num_segments 8 --gd 20 --lr 0.02 \
            --lr_scheduler step --lr_steps  30 45 55 --epochs 60 --batch-size 16 \
            --wd 5e-4 --dropout 0.5 --consensus_type=avg --eval-freq=1 -j 4 --npb

For example, to train TDN-ResNet50 on Kinetics400 with 8 gpus, you can run:

python -m torch.distributed.launch --master_port 12347 --nproc_per_node=8 \
        main.py  kinetics RGB --arch resnet50 --num_segments 8 --gd 20 --lr 0.02 \
        --lr_scheduler step  --lr_steps 50 75 90 --epochs 100 --batch-size 16 \
        --wd 1e-4 --dropout 0.5 --consensus_type=avg --eval-freq=1 -j 4 --npb

Acknowledgements

We especially thank the contributors of the TSN and TSM codebase for providing helpful code.

License

This repository is released under the Apache-2.0. license as found in the LICENSE file.

Citation

If you think our work is useful, please feel free to cite our paper 😆 :

@article{wang2020tdn,
      title={TDN: Temporal Difference Networks for Efficient Action Recognition}, 
      author={Limin Wang and Zhan Tong and Bin Ji and Gangshan Wu},
      journal={arXiv preprint arXiv:2012.10071},
      year={2020}
}

hello doctor.I TRAIN tdn with new data with pretrain model,but it easily go converge,in the first epoch,does something wrong?besides,when i train,it notice below,it is ok? [12/08 15:20:34] TDN INFO: #### Notice: keys that failed to load: {'module.base_model.layer3_bak.3.mse.bn3_smallscale2.num_batches_tracked', 'base_model.layer3_bak.3.mse.bn3_smallscale2.running_var', 'module.base_model.layer2_bak.1.mse.conv3.weight', 'base_model.layer2_bak.2.shift.conv.weight', 'base_model.layer3_bak.1.mse.bn3_smallscale4.running_var', 'module.base_model.layer3_bak.0.bn3.num_batches_tracked', 'module.base_model.layer3_bak.1.mse.bn3.running_var', 'base_model.layer3_bak.5.mse.bn1.weight', 'base_model.resnext_layer1.0.bn1.running_var', 'base_model.layer3_bak.0.bn2.num_batches_tracked', 'module.base_model.layer2_bak.2.conv1.bias', 'module.base_model.resnext_layer1.2.conv2.weight', 'module.base_model.layer3_bak.0.mse.bn3_smallscale2.running_mean', 'base_model.layer2_bak.2.mse.bn3_smallscale2.bias', 'base_model.layer2_bak.3.mse.bn3_smallscale4.bias', 'base_model.layer2_bak.2.mse.bn3_smallscale2.num_batches_tracked', 'module.base_model.resnext_layer1.0.bn3.weight', 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Problems related to model performance

Hi~ I have trained a TDN(ResNet50+8frame) on something-v1 as well as something-v2. However, there exists some gaps between the results we do and the reported one. On something-v2, best Prec@1 is only 61.567. We follow the command and process that you mentioned in README.md. I wonder that maybe some hyper-parameters settings in our experiments is not the same as your experiments.

opened by jwfanDL 10
Training with my own datasets

I follow the steps made my own datasets with kinetics400 format,one GPU, I set batch_size=16,and but i met a question when trained,this is error code: train_loader: <torch.utils.data.dataloader.DataLoader object at 0x7f90f6298460> Traceback (most recent call last): File "main.py", line 361, in main() File "main.py", line 211, in main train_loss, train_top1, train_top5 = train(train_loader, model, criterion, optimizer, epoch=epoch, logger=logger, scheduler=scheduler) File "main.py", line 261, in train for i, (input, target) in enumerate(train_loader): File "/home/shtf/anaconda3/envs/py38/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 363, in next data = self._next_data() File "/home/shtf/anaconda3/envs/py38/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 989, in _next_data return self._process_data(data) File "/home/shtf/anaconda3/envs/py38/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1014, in _process_data data.reraise() File "/home/shtf/anaconda3/envs/py38/lib/python3.8/site-packages/torch/_utils.py", line 395, in reraise raise self.exc_type(msg) UnboundLocalError: Caught UnboundLocalError in DataLoader worker process 0. Original Traceback (most recent call last): File "/home/shtf/anaconda3/envs/py38/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 185, in _worker_loop data = fetcher.fetch(index) File "/home/shtf/anaconda3/envs/py38/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/shtf/anaconda3/envs/py38/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/shtf/ysir/pycharmprojects/TDN-main/ops/dataset.py", line 162, in getitem video_list = decord.VideoReader(video_path) UnboundLocalError: local variable 'video_path' referenced before assignment

Traceback (most recent call last): File "/home/shtf/anaconda3/envs/py38/lib/python3.8/runpy.py", line 194, in _run_module_as_main return _run_code(code, main_globals, None, File "/home/shtf/anaconda3/envs/py38/lib/python3.8/runpy.py", line 87, in _run_code exec(code, run_globals) File "/home/shtf/anaconda3/envs/py38/lib/python3.8/site-packages/torch/distributed/launch.py", line 261, in main() File "/home/shtf/anaconda3/envs/py38/lib/python3.8/site-packages/torch/distributed/launch.py", line 256, in main raise subprocess.CalledProcessError(returncode=process.returncode, subprocess.CalledProcessError: Command '['/home/shtf/anaconda3/envs/py38/bin/python', '-u', 'main.py', '--local_rank=0', 'kinetics', 'RGB', '--arch', 'resnet50', '--num_segments', '8', '--gd', '20', '--lr', '0.02', '--lr_scheduler', 'step', '--lr_steps', '50', '75', '90', '--epochs', '100', '--batch-size', '16', '--wd', '1e-4', '--dropout', '0.5', '--consensus_type=avg', '--eval-freq=1', '-j', '4', '--npb']' returned non-zero exit status 1 I have no idea about this ,thank you

opened by YLiyu 8
BatchSize How to set in a single card？

My test result is 6% lower than yours. How is your batch size set in 8 cards. How do you think batch size should be set in a single card？Thanks for your reply！

opened by 1145335145 7
One question

Hi！ I tested sthv1 on two GTX Titan X as follows: python -m torch.distributed.launch --master_port 12347 --nproc_per_node=2
main.py something RGB --arch resnet50 --num_segments 8 --gd 20 --lr 0.005
--lr_scheduler step --lr_steps 30 45 55 --epochs 60 --batch-size 16
--wd 5e-4 --dropout 0.5 --consensus_type=avg --eval-freq=1 -j 4 --npb But the accuracy in modelzoom is not obtained, In addition, I tried to implement the core code of TDN on the basis of TSM in mmaction2, and only achieved 48.9%. What details did I ignore? Look forward to your reply！

opened by wanlmq6165163 7
One question

Hi, I run the following testing scripts CUDA_VISIBLE_DEVICES=1 python test_models_center_crop.py something --archs='resnet50' --weights checkpoints/best.pth.tar --test_segments=8 --test_crops=1 --batch_size=16 --gpus 0 --output_dir output/8f -j 4 --clip_index=0 in which the best.pth.tar is downloaded from your sthv1 model zoo, but I got the results that Overall Prec@1 0.22% Prec@5 2.40%. I wonder what mistakes might I make...

opened by AllenM97 7
Lower performance on Kinetics using model zoo TDN-ResNet50 checkpoint

I followed your Testing instructions (3-crops, 10-clips) and evaluated model zoo TDN-ResNet50 checkpoint on Kinetics. The only difference is that I use extracted frames as you process Something-V1&V2 instead of raw video inputs you process Kinetics. I ran the Testing twice and got 75.05/91.95 and 75.10/91.97 respectively. I know there is some dataset differences but it should not bring such a big performance gap. Would you kindly check if the Testing setting is the one you obtain the reported performance?

opened by caidonkey 7
Pretrained models

Thanks for sharing the code. After downloading pretrained models, I found that the files is damaged and can not be used. Please check and provide available models.Thanks!

opened by LV0913 6
decord._ffi.base.DECORDError: [16:56:24] /github/workspace/src/video/video_reader.cc:151: Check failed: st_nb >= 0 (-1381258232 vs. 0) ERROR cannot find video stream with wanted index: -1

when I trained in kinetics 400, it happened:

`=> base model: resnet50 kinetics: 400 classes [06/22 16:52:42 TDN]: storing name: TDN__kinetics_RGB_resnet50_avg_segment8_e100

Initializing TSN with base model: resnet50. TSN Configurations: input_modality: RGB num_segments: 8 new_length: 1 consensus_module: avg dropout_ratio: 0.5 img_feature_dim: 256 => base model: resnet50 [06/22 16:52:43 TDN]: [TDN-resnet50]group: first_conv_weight has 1 params, lr_mult: 1, decay_mult: 1 [06/22 16:52:43 TDN]: [TDN-resnet50]group: first_conv_bias has 1 params, lr_mult: 2, decay_mult: 0 [06/22 16:52:43 TDN]: [TDN-resnet50]group: normal_weight has 143 params, lr_mult: 1, decay_mult: 1 [06/22 16:52:43 TDN]: [TDN-resnet50]group: normal_bias has 64 params, lr_mult: 2, decay_mult: 0 [06/22 16:52:43 TDN]: [TDN-resnet50]group: BN scale/shift has 232 params, lr_mult: 1, decay_mult: 0 [06/22 16:52:43 TDN]: [TDN-resnet50]group: custom_ops has 0 params, lr_mult: 1, decay_mult: 1 video number:234619 video number:19760 video number:234619 video number:19760 libibverbs: Warning: couldn't open config directory '/etc/libibverbs.d'. libibverbs: Warning: couldn't open config directory '/etc/libibverbs.d'. [06/22 16:52:54 TDN]: Epoch: [0][0/14663], lr: 0.02000 Time 6.965 (6.965) Data 3.851 (3.851) Loss 5.9819 (5.9819) Prec@1 0.000 (0.000) Prec@5 0.000 (0.000) [06/22 16:53:05 TDN]: Epoch: [0][20/14663], lr: 0.02000 Time 0.951 (0.853) Data 0.000 (0.183) Loss 6.1689 (6.5195) Prec@1 0.000 (0.000) Prec@5 0.000 (0.000) [06/22 16:53:19 TDN]: Epoch: [0][40/14663], lr: 0.02000 Time 0.555 (0.782) Data 0.000 (0.094) Loss 6.0868 (6.3538) Prec@1 0.000 (0.305) Prec@5 0.000 (0.305) [06/22 16:53:34 TDN]: Epoch: [0][60/14663], lr: 0.02000 Time 0.476 (0.761) Data 0.000 (0.063) Loss 6.1435 (6.2322) Prec@1 0.000 (0.205) Prec@5 0.000 (0.820) [06/22 16:53:47 TDN]: Epoch: [0][80/14663], lr: 0.02000 Time 0.908 (0.742) Data 0.000 (0.048) Loss 5.7867 (6.1691) Prec@1 0.000 (0.309) Prec@5 0.000 (0.772) [06/22 16:54:01 TDN]: Epoch: [0][100/14663], lr: 0.02000 Time 0.554 (0.729) Data 0.000 (0.038) Loss 5.7885 (6.1329) Prec@1 0.000 (0.371) Prec@5 0.000 (1.114) [06/22 16:54:14 TDN]: Epoch: [0][120/14663], lr: 0.02000 Time 0.680 (0.719) Data 0.000 (0.032) Loss 6.0279 (6.1143) Prec@1 0.000 (0.310) Prec@5 0.000 (0.930) [06/22 16:54:27 TDN]: Epoch: [0][140/14663], lr: 0.02000 Time 0.491 (0.708) Data 0.000 (0.027) Loss 5.8971 (6.0965) Prec@1 0.000 (0.266) Prec@5 0.000 (1.064) [06/22 16:54:41 TDN]: Epoch: [0][160/14663], lr: 0.02000 Time 0.519 (0.704) Data 0.000 (0.024) Loss 5.8185 (6.0764) Prec@1 0.000 (0.311) Prec@5 12.500 (1.242) [06/22 16:54:54 TDN]: Epoch: [0][180/14663], lr: 0.02000 Time 0.613 (0.702) Data 0.000 (0.021) Loss 5.8592 (6.0648) Prec@1 0.000 (0.345) Prec@5 0.000 (1.312) [06/22 16:55:08 TDN]: Epoch: [0][200/14663], lr: 0.02000 Time 0.519 (0.699) Data 0.000 (0.019) Loss 5.9776 (6.0537) Prec@1 0.000 (0.435) Prec@5 0.000 (1.368) [06/22 16:55:21 TDN]: Epoch: [0][220/14663], lr: 0.02000 Time 0.500 (0.697) Data 0.000 (0.018) Loss 6.0370 (6.0481) Prec@1 0.000 (0.396) Prec@5 0.000 (1.527) [06/22 16:55:35 TDN]: Epoch: [0][240/14663], lr: 0.02000 Time 0.714 (0.698) Data 0.000 (0.016) Loss 5.8629 (6.0379) Prec@1 0.000 (0.363) Prec@5 12.500 (1.556) [06/22 16:55:49 TDN]: Epoch: [0][260/14663], lr: 0.02000 Time 0.565 (0.695) Data 0.000 (0.015) Loss 5.8003 (6.0317) Prec@1 0.000 (0.431) Prec@5 0.000 (1.628) [06/22 16:56:13 TDN]: Epoch: [0][280/14663], lr: 0.02000 Time 0.572 (0.733) Data 0.000 (0.014) Loss 5.8998 (6.0280) Prec@1 0.000 (0.445) Prec@5 0.000 (1.601) [06/22 16:56:27 TDN]: Epoch: [0][300/14663], lr: 0.02000 Time 0.524 (0.731) Data 0.000 (0.013) Loss 5.8075 (6.0214) Prec@1 0.000 (0.415) Prec@5 0.000 (1.620) Traceback (most recent call last): File "main.py", line 361, in main() File "main.py", line 211, in main train_loss, train_top1, train_top5 = train(train_loader, model, criterion, optimizer, epoch=epoch, logger=logger, scheduler=scheduler) File "main.py", line 260, in train for i, (input, target) in enumerate(train_loader): File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 363, in next data = self._next_data() File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 971, in _next_data return self._process_data(data) File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 1014, in _process_data data.reraise() File "/usr/local/lib/python3.6/dist-packages/torch/_utils.py", line 395, in reraise raise self.exc_type(msg) decord._ffi.base.DECORDError: Caught DECORDError in DataLoader worker process 2. Original Traceback (most recent call last): File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/worker.py", line 185, in _worker_loop data = fetcher.fetch(index) File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in data = [self.dataset[idx] for idx in possibly_batched_index] File "/workspace/mnt/storage/kanghaidong/new_video_project/video_project/TDN/ops/dataset.py", line 166, in getitem video_list = decord.VideoReader(video_path) File "/usr/local/lib/python3.6/dist-packages/decord/video_reader.py", line 55, in init uri, ctx.device_type, ctx.device_id, width, height, num_threads, 0, fault_tol) File "/usr/local/lib/python3.6/dist-packages/decord/_ffi/_ctypes/function.py", line 175, in call ctypes.byref(ret_val), ctypes.byref(ret_tcode))) File "/usr/local/lib/python3.6/dist-packages/decord/_ffi/base.py", line 78, in check_call raise DECORDError(err_str) decord._ffi.base.DECORDError: [16:56:24] /github/workspace/src/video/video_reader.cc:151: Check failed: st_nb >= 0 (-1381258232 vs. 0) ERROR cannot find video stream with wanted index: -1` how to solve it?

opened by NEUdeep 6
How long did the model converge in Kinetics400 on 8 V100?

Hi, Here I come again! I've implemented TDN using mmaction2. However, it will take 4 days to finish 100 epochs. I'm wondering if my code is inefficient.

opened by SCZwangxiao 5
where is the short term TDM part?

Hi, thank your for sharing codes. I am reading your code and I only find the long term TDM as mentioned in the paper in base_module.py and the short term TDM is not found, so where is it?

Thank you very much.

opened by Holmes-GU 5
Problem of testing

For 3 crops, 10 clips: the following script should be run 10 times, Is --weights <your_checkpoint_path> always the same? If --weights <your_checkpoint_path> does not change, the result of testing remains the same. If --weights <your_checkpoint_path> changes with each clips, Does that mean i need to train the network 10 times.

CUDA_VISIBLE_DEVICES=0 python3 test_models_three_crops.py kinetics \ --archs='resnet50' --weights <your_checkpoint_path> --test_segments=8 \ --test_crops=3 --batch_size=16 --full_res --gpus 0 --output_dir <your_pkl_path> \ -j 4 --clip_index <your_clip_index>

opened by xiaofengWang-CCNU 4
train new dataset with pretrained model

hello doctor.I TRAIN tdn with new data with pretrain model,but it easily go converge,in the first epoch,does something wrong?besides,when i train,it notice below,it is ok? [12/08 15:20:34] TDN INFO: #### Notice: keys that failed to load: {'module.base_model.layer3_bak.3.mse.bn3_smallscale2.num_batches_tracked', 'base_model.layer3_bak.3.mse.bn3_smallscale2.running_var', 'module.base_model.layer2_bak.1.mse.conv3.weight', 'base_model.layer2_bak.2.shift.conv.weight', 'base_model.layer3_bak.1.mse.bn3_smallscale4.running_var', 'module.base_model.layer3_bak.0.bn3.num_batches_tracked', 'module.base_model.layer3_bak.1.mse.bn3.running_var', 'base_model.layer3_bak.5.mse.bn1.weight', 'base_model.resnext_layer1.0.bn1.running_var', 'base_model.layer3_bak.0.bn2.num_batches_tracked', 'module.base_model.layer2_bak.2.conv1.bias', 'module.base_model.resnext_layer1.2.conv2.weight', 'module.base_model.layer3_bak.0.mse.bn3_smallscale2.running_mean', 'base_model.layer2_bak.2.mse.bn3_smallscale2.bias', 'base_model.layer2_bak.3.mse.bn3_smallscale4.bias', 'base_model.layer2_bak.2.mse.bn3_smallscale2.num_batches_tracked', 'module.base_model.resnext_layer1.0.bn3.weight', 'module.base_model.layer2_bak.0.bn1.num_batches_tracked', 'module.base_model.layer1_bak.2.bn1.bias', 'base_model.layer3_bak.2.bn2.num_batches_tracked', 'base_model.layer3_bak.3.mse.bn3_smallscale4.num_batches_tracked', 'base_model.layer3_bak.5.mse.bn3_smallscale4.running_mean', 'base_model.layer2_bak.2.conv1.bias', 'module.base_model.layer3_bak.0.mse.bn1.bias', 'base_model.layer4_bak.0.mse.bn1.running_mean', 'base_model.layer1_bak.0.downsample.1.bias', 'base_model.layer2_bak.0.bn1.weight', 'base_model.layer2_bak.1.bn2.running_var', 'base_model.layer3_bak.3.bn1.weight', 'base_model.resnext_layer1.2.conv1.weight', 'base_model.resnext_layer1.0.downsample.1.num_batches_tracked', 'base_model.layer3_bak.5.conv1.weight', 'base_model.layer4_bak.0.downsample.1.running_mean', 'module.base_model.layer3_bak.4.bn3.running_mean', 'module.base_model.layer2_bak.1.mse.bn3_smallscale4.running_mean', 'module.base_model.layer3_bak.5.mse.bn3.num_batches_tracked', 'module.base_model.layer3_bak.2.mse.bn1.bias', 'base_model.layer2_bak.2.mse.bn3_smallscale2.weight', 'module.base_model.resnext_layer1.2.bn2.running_var', 'base_model.conv1.bias', 'base_model.layer1_bak.0.bn3.weight', 'module.base_model.layer2_bak.1.mse.bn1.bias', 'base_model.layer3_bak.0.mse.bn3_smallscale2.num_batches_tracked', 'module.base_model.layer3_bak.0.mse.bn3_smallscale2.bias', 'base_model.layer3_bak.2.mse.bn3.weight', 'base_model.layer2_bak.1.mse.bn3_smallscale4.num_batches_tracked', 'module.base_model.layer3_bak.4.bn2.bias', 'base_model.layer4_bak.2.mse.bn3_smallscale2.num_batches_tracked', 'base_model.layer4_bak.2.mse.bn3.running_mean', 'module.base_model.layer4_bak.2.mse.bn1.bias', 'module.base_model.layer1_bak.2.bn1.running_var', 'base_model.layer1_bak.2.conv1.bias', 'base_model.resnext_layer1.0.bn3.running_mean', 'base_model.layer3_bak.0.conv3.weight', 'base_model.layer4_bak.1.conv3.weight', 'module.base_model.layer3_bak.5.conv2.weight', 'base_model.layer4_bak.1.bn2.bias', 'base_model.layer1_bak.1.bn3.bias', 'base_model.layer3_bak.0.bn3.num_batches_tracked', 'module.base_model.layer2_bak.3.bn1.running_var', 'module.base_model.layer2_bak.1.conv3.weight', 'module.base_model.layer4_bak.2.mse.bn3_smallscale2.bias', 'base_model.layer4_bak.1.mse.conv3_smallscale4.weight', 'module.base_model.layer2_bak.1.bn3.running_mean', 'base_model.layer2_bak.1.conv1.bias', 'base_model.resnext_layer1.2.conv2.bias', 'base_model.layer4_bak.1.mse.bn3.running_mean', 'base_model.layer4_bak.2.bn3.num_batches_tracked', 'module.base_model.layer2_bak.1.bn1.weight', 'base_model.layer2_bak.0.downsample.1.weight', 'module.base_model.resnext_layer1.2.bn1.running_var', 'module.base_model.layer3_bak.2.bn1.num_batches_tracked', 'module.base_model.layer4_bak.0.bn3.num_batches_tracked', 'base_model.layer3_bak.4.conv2.weight', 'base_model.layer3_bak.0.mse.bn3.weight', 'module.base_model.layer1_bak.0.bn3.running_var', 'base_model.layer4_bak.1.mse.bn3_smallscale4.weight', 'base_model.layer3_bak.1.mse.bn3_smallscale4.num_batches_tracked', 'base_model.layer2_bak.2.mse.bn3.running_mean', 'base_model.resnext_layer1.0.conv1.weight', 'base_model.layer3_bak.5.conv1.bias', 'base_model.layer2_bak.1.conv2.weight', 'module.base_model.layer1_bak.0.downsample.1.running_var', 'base_model.layer2_bak.1.bn3.weight', 'base_model.layer2_bak.3.bn1.weight', 'module.base_model.layer2_bak.1.bn1.bias', 'module.base_model.layer3_bak.3.mse.bn1.running_mean', 'base_model.layer1_bak.2.bn3.running_var', 'module.base_model.layer2_bak.2.bn1.running_mean', 'base_model.resnext_layer1.1.conv1.bias', 'base_model.layer3_bak.3.bn1.num_batches_tracked', 'module.base_model.resnext_layer1.1.conv3.bias', 'module.base_model.resnext_layer1.0.downsample.1.running_var', 'base_model.layer3_bak.3.bn2.bias', 'module.base_model.layer2_bak.1.bn2.num_batches_tracked', 'module.base_model.layer1_bak.2.bn3.num_batches_tracked', 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'base_model.resnext_layer1.1.conv2.bias', 'module.base_model.layer1_bak.1.bn2.running_var', 'base_model.layer3_bak.2.mse.bn3_smallscale4.running_var', 'base_model.layer4_bak.1.conv2.bias', 'base_model.bn1.running_mean', 'base_model.resnext_layer1.0.conv1.bias', 'base_model.layer2_bak.0.mse.bn3_smallscale2.running_var', 'base_model.layer4_bak.0.downsample.0.weight', 'module.base_model.resnext_layer1.2.bn2.running_mean', 'base_model.layer3_bak.2.bn3.running_var', 'base_model.layer3_bak.2.bn1.bias', 'module.base_model.layer4_bak.0.mse.bn3_smallscale4.bias', 'base_model.layer3_bak.2.mse.bn1.bias', 'base_model.resnext_layer1.0.conv2.weight', 'base_model.layer4_bak.0.shift.conv.weight', 'module.base_model.layer2_bak.0.downsample.1.running_var', 'base_model.layer2_bak.1.mse.bn3_smallscale4.running_var', 'base_model.layer3_bak.3.mse.bn1.running_mean', ...........................

opened by sanwei111 2
frames*crops*clips and train_log

hello，professor. could i please ask u 2 question？ 1.what‘s the meaning of framescropsclips？ frames：the frame fed to model in test？but i actually give 80 frames（a full video） to model，how to become less？ crops：means crop different parts of an original image？ clips：could u please tell me the detail about clip？

2.i have train some new data from your pretrained model。a little weird，i set epoch to 35，but in the first epoch，its loss cames to the least：0.67，and almost keep the same，and the Testing Results almost keeps the same，how could it be？

opened by sanwei111 0
About hmdb51 Dataset

Hello, I want to train on hmdb51 dataset, so I wonder whether to make the dataset format like something-something or kinetics400 as you cited in the data preparation part. Thank you a lot.

opened by yyhyyh17 0

TDN: Temporal Difference Networks for Efficient Action Recognition

Related tags

Overview

TDN: Temporal Difference Networks for Efficient Action Recognition

Overview

Prerequisites

Data Preparation

Model Zoo

Something-Something-V1

Something-Something-V2

Kinetics400

Testing

Training

Acknowledgements

License

Citation

Comments

Owner

Multimedia Computing Group, Nanjing University

Official Pytorch Implementation of 'Learning Action Completeness from Points for Weakly-supervised Temporal Action Localization' (ICCV-21 Oral)

Code for the ICCV 2021 paper "Pixel Difference Networks for Efficient Edge Detection" (Oral).

Spatial Temporal Graph Convolutional Networks (ST-GCN) for Skeleton-Based Action Recognition in PyTorch

CVPR2021: Temporal Context Aggregation Network for Temporal Action Proposal Refinement

Official implementation of ACTION-Net: Multipath Excitation for Action Recognition (CVPR'21).

The official TensorFlow implementation of the paper Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition

Official PyTorch implementation of "Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics".

Learning Representational Invariances for Data-Efficient Action Recognition

Allows including an action inside another action (by preprocessing the Yaml file). This is how composite actions should have worked.

Human Action Controller - A human action controller running on different platforms.

Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19

Code for CVPR2021 paper "Learning Salient Boundary Feature for Anchor-free Temporal Action Localization"

A pytorch-version implementation codes of paper: "BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation"

AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation

Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks

Implementation of temporal pooling methods studied in [ICIP'20] A Comparative Evaluation Of Temporal Pooling Methods For Blind Video Quality Assessment

Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-local Spatial-Temporal Similarity

[CVPR 2021] Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion