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--- |
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title: Adult Image Detector |
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emoji: 🚨 |
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colorFrom: yellow |
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colorTo: green |
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sdk: gradio |
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sdk_version: 4.42.0 |
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app_file: app.py |
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pinned: false |
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license: mit |
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--- |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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# Adult Image Detector |
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## Model Description |
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This model is a custom-trained version of YOLOv9-e, pre-trained on a custom dataset. YOLOv9 (You Only Look Once version 9) is a state-of-the-art object detection model known for its speed and accuracy. |
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## Model Details |
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- **Model Architecture:** YOLOv9-e |
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- **Number of Layers:** 1,119 |
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- **Number of Parameters:** 69,366,830 |
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- **GFLOPs:** 243.4 |
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## Training |
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The model was trained for 10 epochs on a custom dataset. The training process showed consistent improvement in performance metrics. |
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### Training Hyperparameters |
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- **Initial Learning Rate (lr0):** 0.070011 |
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- **Final Learning Rate (lr1, lr2):** 0.00208 |
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### Training Results |
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| Metric | Initial Value (Epoch 0) | Final Value (Epoch 9) | |
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|--------|-------------------------|------------------------| |
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| train/box_loss | 1.8995 | 1.4264 | |
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| train/cls_loss | 2.644 | 1.1627 | |
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| train/dfl_loss | 1.9846 | 1.6321 | |
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| metrics/precision | 0.70196 | 0.69025 | |
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| metrics/recall | 0.44274 | 0.69178 | |
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| metrics/mAP_0.5 | 0.45088 | 0.7167 | |
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| metrics/mAP_0.5:0.95 | 0.27358 | 0.47964 | |
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## Performance |
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The model showed significant improvement over the course of training: |
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- **[email protected]:** Increased from 0.45088 to 0.7167 |
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- **[email protected]:0.95:** Improved from 0.27358 to 0.47964 |
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- **Precision:** Maintained around 0.69-0.70 |
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- **Recall:** Substantially improved from 0.44274 to 0.69178 |
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## Usage |
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This model can be loaded and used with YOLOv5 compatible frameworks. Here's an example of how to load the model: |
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```python |
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from ultralytics import YOLO |
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model = YOLO('path/to/your/model.pt') |
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results = model('path/to/image.jpg') |
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``` |
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## Limitations and Biases |
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As this model was trained on a custom dataset, it may have biases or limitations specific to that dataset. Users should evaluate the model's performance on their specific use case before deployment. |
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## Additional Information |
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For more details on the YOLOv9 architecture and its capabilities, please refer to the official YOLOv9 documentation and research paper. |
