packages/tasks/src/video-classification/about.md

Use Cases

Video classification models can be used to categorize what a video is all about.

Activity Recognition

Video classification models are used to perform activity recognition which is useful for fitness applications. Activity recognition is also helpful for vision-impaired individuals especially when they're commuting.

Video Search

Models trained in video classification can improve user experience by organizing and categorizing video galleries on the phone or in the cloud, on multiple keywords or tags.

Inference

Below you can find code for inferring with a pre-trained video classification model.

from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification
from pytorchvideo.transforms import UniformTemporalSubsample
from pytorchvideo.data.encoded_video import EncodedVideo


# Load the video.
video = EncodedVideo.from_path("path_to_video.mp4")
video_data = video.get_clip(start_sec=0, end_sec=4.0)["video"]

# Sub-sample a fixed set of frames and convert them to a NumPy array.
num_frames = 16
subsampler = UniformTemporalSubsample(num_frames)
subsampled_frames = subsampler(video_data)
video_data_np = subsampled_frames.numpy().transpose(1, 2, 3, 0)

# Preprocess the video frames.
inputs = feature_extractor(list(video_data_np), return_tensors="pt")

# Run inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits

# Model predicts one of the 400 Kinetics 400 classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
# `eating spaghetti` (if you chose this video:
# https://hf.co/datasets/nielsr/video-demo/resolve/main/eating_spaghetti.mp4)

Useful Resources

• Developing a simple video classification model
• Video classification with Transformers
• Building a video archive
• Video classification task guide

Creating your own video classifier in minutes

• Fine-tuning tutorial notebook (PyTorch)