"""2. Fine-tuning SOTA video models on your own dataset ======================================================= Fine-tuning is an important way to obtain good video models on your own data when you don't have large annotated dataset or don't have the computing resources to train a model from scratch for your use case. In this tutorial, we provide a simple unified solution. The only thing you need to prepare is a text file containing the information of your videos (e.g., the path to your videos), we will take care of the rest. You can start fine-tuning from many popular pre-trained models (e.g., I3D, R2+1D, SlowFast and TPN) using a single command line. """ ###################################################################### # Custom DataLoader # ------------------ # # The first and only thing you need to prepare is the data annotation files ``train.txt`` and ``val.txt``. # We provide a general dataloader for you to use on your own dataset. # Your data can be stored in any hierarchy, and the ``train.txt`` should look like: # # :: # # video_001.mp4 200 0 # video_001.mp4 200 0 # video_002.mp4 300 0 # video_003.mp4 100 1 # video_004.mp4 400 2 # ...... # video_100.mp4 200 10 # # As you can see, there are three items in each line, separated by spaces. # The first item is the path to your training videos, e.g., video_001.mp4. # The second item is the number of frames in each video. But you can put any number here # because our video loader will compute the number of frames again automatically during training. # The third item is the label of that video, e.g., 0. # ``val.txt`` looks the same as ``train.txt`` in terms of format. # # # Once you prepare the ``train.txt`` and ``val.txt``, you are good to go. # In this tutorial, we will use I3D model and Something-something-v2 dataset as an example. # Suppose you have Something-something-v2 dataset and you don't want to train an I3D model from scratch. # First, prepare the data anotation files as mentioned above. # Second, follow this configuration file `i3d_resnet50_v1_custom.yaml `_. # Specifically, you just need to change the data paths and number of classes in that yaml file. # # :: # # TRAIN_ANNO_PATH: '/home/ubuntu/data/sthsthv2/sthsthv2_train.txt' # VAL_ANNO_PATH: '/home/ubuntu/data/sthsthv2/sthsthv2_val.txt' # TRAIN_DATA_PATH: '/home/ubuntu/data/sthsthv2/20bn-something-something-v2/' # VAL_DATA_PATH: '/home/ubuntu/data/sthsthv2/20bn-something-something-v2/' # NUM_CLASSES: 174 # # If you want to tune other parameters, it is also easy to do. # Change the learning rate, batch size, clip lenght according to your use cases. Usually a small learning rate is preferred since the model initialization is decent. ###################################################################### # We also support finetuning on other models, e.g., # `resnet50_v1b_custom.yaml `_, # `slowfast_4x16_resnet50_custom.yaml `_, # `tpn_resnet50_f32s2_custom.yaml `_, # `r2plus1d_v1_resnet50_custom.yaml `_, # `i3d_slow_resnet50_f32s2_custom.yaml `_. # Try fine-tuning these SOTA video models on your own dataset and see how it goes. # # If you would like to extract good video features on your datasets, # feel free to read the next `tutorial on feature extraction `__.