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Video Classification

The Task

Typically, Video Classification refers to the task of producing a label for actions identified in a given video. The task is to predict which class the video clip belongs to.

Lightning Flash VideoClassifier and VideoClassificationData classes internally rely on PyTorchVideo.


Example

Let’s develop a model to classifying video clips of Humans performing actions (such as: archery , bowling, etc.). We’ll use data from the Kinetics dataset. Here’s an outline of the folder structure:

video_dataset
├── train
│   ├── archery
│   │   ├── -1q7jA3DXQM_000005_000015.mp4
│   │   ├── -5NN5hdIwTc_000036_000046.mp4
│   │   ...
│   ├── bowling
│   │   ├── -5ExwuF5IUI_000030_000040.mp4
│   │   ├── -7sTNNI1Bcg_000075_000085.mp4
│   ... ...
└── val
    ├── archery
    │   ├── 0S-P4lr_c7s_000022_000032.mp4
    │   ├── 2x1lIrgKxYo_000589_000599.mp4
    │   ...
    ├── bowling
    │   ├── 1W7HNDBA4pA_000002_000012.mp4
    │   ├── 4JxH3S5JwMs_000003_000013.mp4
    ... ...

Once we’ve downloaded the data using download_data(), we create the VideoClassificationData. We select a pre-trained backbone to use for our VideoClassifier and fine-tune on the Kinetics data. The backbone can be any model from the PyTorchVideo Model Zoo. We then use the trained VideoClassifier for inference. Finally, we save the model. Here’s the full example:

import os

import torch

import flash
from flash.core.data.utils import download_data
from flash.video import VideoClassificationData, VideoClassifier

# 1. Create the DataModule
# Find more datasets at https://pytorchvideo.readthedocs.io/en/latest/data.html
download_data("https://pl-flash-data.s3.amazonaws.com/kinetics.zip", "./data")

datamodule = VideoClassificationData.from_folders(
    train_folder=os.path.join(os.getcwd(), "data/kinetics/train"),
    val_folder=os.path.join(os.getcwd(), "data/kinetics/val"),
    clip_sampler="uniform",
    clip_duration=1,
    decode_audio=False,
)

# 2. Build the task
model = VideoClassifier(backbone="x3d_xs", num_classes=datamodule.num_classes, pretrained=False)

# 3. Create the trainer and finetune the model
trainer = flash.Trainer(max_epochs=3, gpus=torch.cuda.device_count())
trainer.finetune(model, datamodule=datamodule, strategy="freeze")

# 4. Make a prediction
predictions = model.predict(os.path.join(os.getcwd(), "data/kinetics/predict"))
print(predictions)

# 5. Save the model!
trainer.save_checkpoint("video_classification.pt")

Flash Zero

The video classifier can be used directly from the command line with zero code using Flash Zero. You can run the above example with:

flash video_classification

To view configuration options and options for running the video classifier with your own data, use:

flash video_classification --help
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