Anomaly Detection Model
The model achieved the following scores on the evaluation dataset:
Model source
- Kind
- Other
- File size
- 338 MB
- Base model
- google/vivit-b-16x2
- Version
- vRevision b29a790
- Updated
- Sep 18, 2026
- Downloads
- 0
- Source
- Hugging Face
Source description
Model Performance
The model achieved the following scores on the evaluation dataset:
- Accuracy: 94.6%
- F1 Score: 94.3%
- Recall: 94.6%
- Precision: 94.5%
Intended Use & Limitations
- Best for: CCTV footage analysis, anomaly detection
- Not suitable for: Non-surveillance video types, real-time processing with limited hardware
Training Details
- Learning Rate: 5e-6
- Batch Size: 2
- Optimizer: Adam
- Training Steps: 4176
Framework Versions
- Transformers: 4.39.3
- PyTorch: 2.1.2
- Datasets: 2.18.0
Sources
1 source
Sources
1 sourceVerified Sep 18
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- license: mit base_model: google/vivit-b-16x2 tags: - cctv-surveillance - video-classification metrics: - accuracy - f1 - recall - precision --- ## Model Performance The model achieved the following scores on the evaluation dataset: - **Accuracy**: 94.6% - **F1 Score**: 94.3% - **Recall**: 94.6% - **Precision**: 94.5% ## Intended Use & Limitations - **Best for:** CCTV footage analysis, anomaly detection - **Not suitable for:** Non-surveillance video types, real-time processing with limited hardware ## Training Details - **Learning Rate:** 5e-6 - **Batch Size:** 2 - **Optimizer:** Adam - **Training Steps:** 4176 ## Framework Versions - Transformers: 4.39.3 - PyTorch: 2.1.2 - Datasets: 2.18.0
Source context: 0 downloads · 0 likes · Pipeline video-classification · Repo manubunnyy/anomaly_detection_model
