10 code Action Recognition via Video Swin Transformer Pipeline скачать в хорошем качестве

10 code Action Recognition via Video Swin Transformer Pipeline

This code implements a video classification pipeline using the Video Swin Transformer, a state-of-the-art architecture for human action recognition. 1. Dataset Setup and Pre-processing The code prepares the Kinetics6-mini dataset for training and evaluation. decord.VideoReader: This library is used for high-performance video loading and frame extraction. np.linspace: To ensure every video has exactly 32 frames (the input size required by the network), this function generates equally spaced frame indexes. If a video is shorter than 32 frames, it effectively repeats frames to fill the gap. format_frames: Converts raw video frames into a format compatible with TensorFlow tensors. 2. Video Swin Transformer Architecture The model uses a hierarchical vision transformer specifically designed for video data. Feature Extractor: It leverages a pre-trained Video Swin Transformer backbone, which uses "shifted windows" to capture both spatial information (within a frame) and temporal information (across frames). Pre-trained Weights: The code downloads and loads videoswin_base_kinetics400_classifier.weights.h5, allowing the model to benefit from patterns learned on the massive Kinetics-400 dataset. 3. Data Generators (prepare_dataset) This function manages how data is fed into the model during training. Input Parameters: data: A list containing tuples of video file paths and their corresponding integer class labels. batch_size: Set to 4, defining how many videos are processed at once. frame_count: Fixed at 32, which is the temporal depth the model expects. 4. Execution and Performance Hardware: The script is designed to run on a T4 GPU in Google Colab to handle the heavy computational load of 3D transformers. Accuracy: For the Kinetics6-mini dataset, this implementation typically achieves a test accuracy between 80% and 100%.