VideoMaev2 Giant Nexar Solution

# Example usage (pseudo-code)
from transformers import VideoMAEForVideoClassification
import torch

model = VideoMAEForVideoClassification.from_pretrained("zhiyaowang/VideoMaev2-giant-nexar-solution")
# Process video frames (16 frames recommended)
frames = preprocess_video(video_path)  # Shape: [1, 16, 3, 224, 224]
with torch.no_grad():
    outputs = model(frames)
probability = torch.softmax(outputs.logits / 2.0, dim=1)  # Temperature scaling T=2.0

Model Training

Data Processing

• Frame Extraction & Timestamps: Extract frame sequences and timestamps from each video.
• Sliding Window: Applied a sliding window approach with 16 frames (window size) and 2 frames (stride).
• Label Assignment: Windows with their last frame within 1.5 seconds before a collision/near-miss event were labeled positive.
• Data Balancing: Randomly undersampled negative

--- language: en license: mit tags: - video - video-classification - videomae - collision-prediction - kaggle datasets: - nexar - nexar-ai/nexar_collision_prediction base_model: - OpenGVLab/VideoMAEv2-giant --- # VideoMAE-based Vehicle Collision Prediction Solution ## Model Description This repository contains a pretrained VideoMAEv2-giant model fine-tuned for the Nexar Safe Driving Video Analysis competition. The model is designed to predict collision and near-miss risks in driving videos. **Performance**: 4th place on the Kaggle public leaderboard with a score of 0.886. ## Usage The model takes video frames as input and outputs a probability score indicating the likelihood of an imminent collision or near-miss event. ```python # Example usage (pseudo-code) from transformers import VideoMAEForVideoClassification import torch model = VideoMAEForVideoClassification.from_pretrained("zhiyaowang/VideoMaev2-giant-nexar-solution") # Process video frames (16 frames recommended) frames = preprocess_video(video_path) # Shape: [1, 16, 3, 224, 224] with torch.no_grad(): outputs = model(frames) probability = torch.softmax(outputs.logits / 2.0, dim=1) # Temperature scaling T=2.0 ``` ## Model Training ### Data Processing - **Frame Extraction & Timestamps**: Extract frame sequences and timestamps from each video. - **Sliding Window**: Applied a sliding window approach with 16 frames (window size) and 2 frames (stride). - **Label Assignment**: Windows with their last frame within 1.5 seconds before a collision/near-miss event were labeled positive. - **Data Balancing**: Randomly undersampled negative