Nikeytas/videomae-crime-detector-production-v1

Nikeytas/Videomae Crime Detector Production V1

This model is a fine-tuned version of MCG-NJU/videomae-base on the UCF Crime dataset with event-based binary classification. It achieves the following results on the evaluation set:

• —Loss: 0.8070
• —Accuracy: 0.6250
• —Precision: 0.6351
• —Recall: 0.6250
• —F1 Score: 0.6114

🎯 Model Overview

This VideoMAE model has been fine-tuned for binary violence detection in video content. The model classifies videos into two categories:

• —Violent Crime (1): Videos containing violent criminal activities
• —Non-Violent Incident (0): Videos with non-violent or normal activities

The model is based on the VideoMAE architecture and has been specifically trained on a curated subset of the UCF Crime dataset with event-based categorization for realistic crime detection scenarios.

📊 Dataset & Training

Dataset Composition

Total Videos: 300

• —Violent Crime Videos: 150
• —Non-Violent Incident Videos: 150

Class Balance: 50.0% violent crimes

Event Distribution:

• —Abuse: 34 videos
• —Arrest: 36 videos
• —Arson: 46 videos
• —Assault: 36 videos
• —Burglary: 70 videos
• —Explosion: 24 videos
• —Fighting: 30 videos
• —RoadAccidents: 86 videos
• —Robbery: 98 videos
• —Shoplifting: 36 videos
• —Stealing: 62 videos

Data Splits:

• —Training: 192 videos
• —Validation: 48 videos
• —Test: 60 videos

🎯 Performance

Performance Metrics

Validation Performance:

• —eval_loss: 0.8070
• —eval_accuracy: 0.6250
• —eval_precision: 0.6351
• —eval_recall: 0.6250
• —eval_f1: 0.6114
• —eval_runtime: 6.4319
• —eval_samples_per_second: 7.4630
• —eval_steps_per_second: 3.7310
• —epoch: 10.0000

Test Performance:

• —eval_loss: 0.6541
• —eval_accuracy: 0.6667
• —eval_precision: 0.6667
• —eval_recall: 0.6667
• —eval_f1: 0.6667
• —eval_runtime: 8.0508
• —eval_samples_per_second: 7.4530
• —eval_steps_per_second: 3.7260
• —epoch: 10.0000

Training Information:

• —Training Time: 19.8 minutes
• —Best Accuracy Achieved: 0.6667
• —Model Architecture: VideoMAE Base (fine-tuned)
• —Fine-tuning Approach: Event-based binary classification

🚀 Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

• —Learning Rate: 5e-05
• —Train Batch Size: 2
• —Eval Batch Size: 2
• —Optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
• —LR Scheduler Type: Linear
• —Training Epochs: 10
• —Weight Decay: 0.01

Training Results

Framework Versions

• —Transformers: 4.30.2+
• —PyTorch: 2.0.1+
• —Datasets: Latest
• —Device: Apple Silicon MPS / CUDA / CPU (Auto-detected)

🚀 Quick Start

Installation

pip install transformers torch torchvision opencv-python pillow

Basic Usage

import torch
from transformers import AutoModelForVideoClassification, AutoProcessor
import cv2
import numpy as np

# Load model and processor
model = AutoModelForVideoClassification.from_pretrained("Nikeytas/videomae-crime-detector-production-v1")
processor = AutoProcessor.from_pretrained("Nikeytas/videomae-crime-detector-production-v1")

# Process video
def classify_video(video_path, num_frames=16):
    # Extract frames
    cap = cv2.VideoCapture(video_path)
    frames = []
    
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    indices = np.linspace(0, total_frames - 1, num_frames, dtype=int)
    
    for idx in indices:
        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
        ret, frame = cap.read()
        if ret:
            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            frames.append(frame_rgb)
    
    cap.release()
    
    # Process with model
    inputs = processor(frames, return_tensors="pt")
    
    with torch.no_grad():
        outputs = model(**inputs)
        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
        predicted_class = torch.argmax(predictions, dim=-1).item()
        confidence = predictions[0][predicted_class].item()
    
    label = "Violent Crime" if predicted_class == 1 else "Non-Violent"
    return label, confidence

# Example usage
video_path = "path/to/your/video.mp4"
prediction, confidence = classify_video(video_path)
print(f"Prediction: {prediction} (Confidence: {confidence:.3f})")

Batch Processing

import os
from pathlib import Path

def process_video_directory(video_dir, output_file="results.txt"):
    results = []
    
    for video_file in Path(video_dir).glob("*.mp4"):
        try:
            prediction, confidence = classify_video(str(video_file))
            results.append({
                "file": video_file.name,
                "prediction": prediction,
                "confidence": confidence
            })
            print(f"✅ {video_file.name}: {prediction} ({confidence:.3f})")
        except Exception as e:
            print(f"❌ Error processing {video_file.name}: {e}")
    
    # Save results
    with open(output_file, "w") as f:
        for result in results:
            f.write(f"{result['file']}: {result['prediction']} ({result['confidence']:.3f})\n")
    
    return results

# Process all videos in a directory
results = process_video_directory("./videos/")

📈 Technical Specifications

• —Base Model: MCG-NJU/videomae-base
• —Architecture: Vision Transformer (ViT) adapted for video
• —Input Resolution: 224x224 pixels per frame
• —Temporal Resolution: 16 frames per video clip
• —Output Classes: 2 (Binary classification)
• —Training Framework: HuggingFace Transformers
• —Optimization: AdamW optimizer with learning rate 5e-5

⚠️ Limitations

1. 1.Dataset Scope: Trained on a subset of UCF Crime dataset - may not generalize to all types of violence
2. 2.Temporal Context: Uses 16-frame clips which may miss context in longer sequences
3. 3.Environmental Bias: Performance may vary with different lighting, camera angles, and video quality
4. 4.False Positives: May misclassify intense but non-violent activities (sports, action movies)
5. 5.Real-time Performance: Processing time depends on hardware capabilities

🔒 Ethical Considerations

Intended Use

• —Primary: Research and development in video analysis
• —Secondary: Security system enhancement with human oversight
• —Educational: Computer vision and AI safety research

Prohibited Uses

• —Surveillance without consent: Do not use for unauthorized monitoring
• —Discriminatory profiling: Avoid bias against specific groups or communities
• —Automated punishment: Never use for automated legal or disciplinary actions
• —Privacy violation: Respect privacy laws and individual rights

Bias and Fairness

• —Model trained on specific dataset that may not represent all populations
• —Regular evaluation needed for bias detection and mitigation
• —Human oversight required for critical applications
• —Consider demographic representation in deployment scenarios

📝 Model Card Information

• —Developed by: Research Team
• —Model Type: Video Classification (Binary)
• —Training Data: UCF Crime Dataset (Subset)
• —Training Date: 2025-06-01 23:46:55 UTC
• —Evaluation Metrics: Accuracy, Precision, Recall, F1-Score
• —Intended Users: Researchers, Security Professionals, Developers

📚 Citation

If you use this model in your research, please cite:

@misc{Nikeytas_videomae_crime_detector_production_v1,
    title={VideoMAE Fine-tuned for Crime Detection},
    author={Research Team},
    year={2024},
    publisher={Hugging Face},
    url={https://huggingface.co/Nikeytas/videomae-crime-detector-production-v1}
}

🤝 Contributing

We welcome contributions to improve the model! Please:

1. 1.Report issues with specific examples
2. 2.Suggest improvements for bias reduction
3. 3.Share evaluation results on new datasets
4. 4.Contribute to documentation and examples

📞 Contact

For questions, issues, or collaboration opportunities, please open an issue in the model repository or contact the development team.

Last updated: 2025-06-01 23:46:55 UTC Model version: 1.0 Framework: HuggingFace Transformers