ADD ReadMe.md

README.md

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 title: Adult Image Detector
-emoji: 📚
+emoji: 🚨
 colorFrom: yellow
 colorTo: green
 sdk: gradio
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 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Adult Image Detector
+
+## Model Description
+
+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.
+
+## Model Details
+
+- **Model Architecture:** YOLOv9-e
+- **Number of Layers:** 1,119
+- **Number of Parameters:** 69,366,830
+- **GFLOPs:** 243.4
+
+## Training
+
+The model was trained for 10 epochs on a custom dataset. The training process showed consistent improvement in performance metrics.
+
+### Training Hyperparameters
+
+- **Initial Learning Rate (lr0):** 0.070011
+- **Final Learning Rate (lr1, lr2):** 0.00208
+
+### Training Results
+
+| Metric | Initial Value (Epoch 0) | Final Value (Epoch 9) |
+|--------|-------------------------|------------------------|
+| train/box_loss | 1.8995 | 1.4264 |
+| train/cls_loss | 2.644 | 1.1627 |
+| train/dfl_loss | 1.9846 | 1.6321 |
+| metrics/precision | 0.70196 | 0.69025 |
+| metrics/recall | 0.44274 | 0.69178 |
+| metrics/mAP_0.5 | 0.45088 | 0.7167 |
+| metrics/mAP_0.5:0.95 | 0.27358 | 0.47964 |
+
+## Performance
+
+The model showed significant improvement over the course of training:
+
+- **mAP@0.5:** Increased from 0.45088 to 0.7167
+- **mAP@0.5:0.95:** Improved from 0.27358 to 0.47964
+- **Precision:** Maintained around 0.69-0.70
+- **Recall:** Substantially improved from 0.44274 to 0.69178
+
+## Usage
+
+This model can be loaded and used with YOLOv5 compatible frameworks. Here's an example of how to load the model:
+
+```python
+from ultralytics import YOLO
+
+model = YOLO('path/to/your/model.pt')
+results = model('path/to/image.jpg')
+```
+
+## Limitations and Biases
+
+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.
+
+## Additional Information
+
+For more details on the YOLOv9 architecture and its capabilities, please refer to the official YOLOv9 documentation and research paper.