This repo contains several models for video action recognition, including C3D, R2Plus1D, R3D, inplemented using PyTorch (0.4.0). Currently, we train these models on UCF101 and HMDB51 datasets. More models and datasets will be available soon!
Note: An interesting online web game based on C3D model is in here.
The code was tested with Anaconda and Python 3.5. After installing the Anaconda environment:
Clone the repo:
git clone https://github.com/jfzhang95/pytorch-video-recognition.git cd pytorch-video-recognition
For PyTorch dependency, see pytorch.org for more details.
For custom dependencies:
conda install opencv pip install tqdm scikit-learn tensorboardX
Configure your dataset and pretrained model path in mypath.py.
You can choose different models and datasets in train.py.
To train the model, please do:
I used two different datasets: UCF101 and HMDB.
Dataset directory tree is shown below
Make sure to put the files as the following structure:
UCF-101 ├── ApplyEyeMakeup │ ├── v_ApplyEyeMakeup_g01_c01.avi │ └── ... ├── ApplyLipstick │ ├── v_ApplyLipstick_g01_c01.avi │ └── ... └── Archery │ ├── v_Archery_g01_c01.avi │ └── ...
After pre-processing, the output dir's structure is as follows:
ucf101 ├── ApplyEyeMakeup │ ├── v_ApplyEyeMakeup_g01_c01 │ │ ├── 00001.jpg │ │ └── ... │ └── ... ├── ApplyLipstick │ ├── v_ApplyLipstick_g01_c01 │ │ ├── 00001.jpg │ │ └── ... │ └── ... └── Archery │ ├── v_Archery_g01_c01 │ │ ├── 00001.jpg │ │ └── ... │ └── ...
Note: HMDB dataset's directory tree is similar to UCF101 dataset's.
These models were trained in machine with NVIDIA TITAN X 12gb GPU. Note that I splited train/val/test data for each dataset using sklearn. If you want to train models using official train/val/test data, you can look in dataset.py, and modify it to your needs.
Currently, I only train C3D model in UCF and HMDB datasets. The train/val/test accuracy and loss curves for each experiment are shown below:
Experiments for other models will be updated soon ...