README.md

metadata

title: Video Human Fall Detection With TimeSformer
emoji: 🌍
colorFrom: blue
colorTo: pink
sdk: gradio
sdk_version: 5.25.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: Fall detector with TimeSformer

Video Human Detection Demo using TimeSformer

This is a Hugging Face Spaces demo project that uses TimeSformer – a video transformer model – for video-based human detection (action recognition). In this demo, we use the pre-trained model microsoft/timesformer-base-finetuned-k400 from Hugging Face, which has been fine‑tuned on the Kinetics‑400 dataset. The model is capable of classifying a video into one of 400 human action categories.

Overview

• Model: We use a TimeSformer model (microsoft/timesformer-base-finetuned-k400) to classify video clips.
• Feature Extractor: The demo employs the Hugging Face AutoFeatureExtractor for video to process and prepare video frames.
• Inference: The model outputs a set of predicted action labels with scores. These predictions help detect human actions in the video.
• Interface: Built with Gradio, the demo lets the user upload a video file. The application extracts frames from the video, processes them with the model, and displays the top action predictions.

Setup and Deployment

1. Requirements: See requirements.txt for the list of required packages.
2. Run Locally: You can run the demo locally using:

   python app.py
   

3. Deploy on Hugging Face Spaces:
   Simply push these files to a new repository under HF Spaces. The app is designed to run with ZeroGPU if available and it is fully compatible with CPU-only environments.

Notes

• Video Preprocessing: The demo extracts frames using OpenCV and passes them to the feature extractor. The number of frames and the resolution are set to default values that can be adjusted.
• Model Performance: TimeSformer is computationally heavy – for real-time use, consider using a smaller or distilled variant, or reduce the number of frames processed.
• ZeroGPU Support: The app uses the @spaces.GPU decorator (from the HF Spaces ZeroGPU environment) if available; otherwise, it will run on CPU.

Enjoy testing human detection in videos with this demo!