A deep learning library for video understanding research

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Based on PyTorch

Built using PyTorch. Makes it easy to use all the PyTorch-ecosystem components.

Reproducible Model Zoo

Variety of state of the art pretrained video models and their associated benchmarks that are ready to use.

Efficient Video Components

Video-focused fast and efficient components that are easy to use. Supports accelerated inference on hardware.

Get Started

  1. Install pytorchvideo (Confirm requirements following the instructions here)
    pip install pytorchvideo
  2. Try Video classification with Model Zoo (For detailed instructions, refer to the PyTorchVideo Model Zoo Inference Tutorial
    # Import all the required components
...

# Load pre-trained model 
model = torch.hub.load('facebookresearch/pytorchvideo', 'slow_r50', pretrained=True)

# Load video
video = EncodedVideo.from_path('some_video.avi')

# Compose video data transforms
transform =  ApplyTransformToKey(
    key="video",
    transform=Compose(
        [
            UniformTemporalSubsample(num_frames),
            Lambda(lambda x: x/255.0),
            NormalizeVideo(mean, std),
            ShortSideScale(
                size=side_size
            ),
            CenterCropVideo(crop_size=(crop_size, crop_size))
        ]
    ),
)

# Get clip
clip_start_sec = 0.0 # secs
clip_duration = 2.0 # secs
video_data = video.get_clip(start_sec=clip_start_sec, end_sec=clip_start_sec + clip_duration)
video_data = transform(video_data)

# Generate top 5 predictions
preds = torch.nn.functional.softmax(preds)
pred_class_ids = preds.topk(k=5).indices