cnn-lstm:在Pytorch中实现的CNN LSTM架构用于视频分类

# CNN LSTM Implementation of CNN LSTM with Resnet backend for Video Classification  # Getting Started ## Prerequisites * PyTorch (ver. 0.4+ required) * FFmpeg, FFprobe * Python 3 ### Try on your own dataset ``` mkdir data mkdir data/video_data ``` Put your video dataset inside data/video_data It should be in this form -- ``` + data + video_data - bowling - walking + running - running0.avi - running.avi - runnning1.avi ``` Generate Images from the Video dataset ``` ./utils/generate_data.sh ``` ## Train Once you have created the dataset, start training -> ``` python main.py --use_cuda --gpu 0 --batch_size 8 --n_epochs 100 --num_workers 0 --annotation_path ./data/annotation/ucf101_01.json --video_path ./data/image_data/ --dataset ucf101 --sample_size 150 --lr_rate 1e-4 --n_classes <num_classes> ``` ## Note * All the weights will be saved to the snapshots folder * To resume Training from any checkpoint, Use ``` --resume_path <path-to-model> ``` ## Tensorboard Visualisation(Training for 4 labels from UCF-101 Dataset)  ## Inference ``` python inference.py --annotation_path ./data/annotation/ucf101_01.json --dataset ucf101 --model cnnlstm --n_classes <num_classes> --resume_path <path-to-model.pth> ``` ## References * https://github.com/kenshohara/video-classification-3d-cnn-pytorch * https://github.com/HHTseng/video-classification ## License This project is licensed under the MIT License