Upload README.md

README.md

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----
-tags:
-- ml-intern
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+# Cursor Detection YOLOv8n
 
-# AdithyaSK/cursor-detection-yolov8n
+A YOLOv8n model trained to detect mouse cursors in screenshots and video frames.
 
-<!-- ml-intern-provenance -->
-## Generated by ML Intern
+## Training Details
 
-This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.
+- **Base Model:** YOLOv8n (3.2M parameters)
+- **Training Data:** Synthetic dataset generated by compositing 366 different cursor types onto 1688 website screenshots
+- **Dataset Size:** 500 train / 100 val / 50 test
+- **Image Size:** 640x640
+- **Epochs:** 30
+- **Hardware:** NVIDIA T4 GPU
 
-- Try ML Intern: https://smolagents-ml-intern.hf.space
-- Source code: https://github.com/huggingface/ml-intern
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| mAP50 | 92.1% |
+| mAP50-95 | 58.2% |
+| Precision | 84.8% |
+| Recall | 89.5% |
+
+## Dataset Generation
+
+The synthetic dataset was created by:
+1. Loading cursor images from [Fraser/cursors](https://huggingface.co/datasets/Fraser/cursors) (366 cursor types with hotspot info)
+2. Loading background screenshots from [naorm/website-screenshots](https://huggingface.co/datasets/naorm/website-screenshots)
+3. Compositing cursors at random positions with alpha blending
+4. Generating YOLO format bounding box labels
 
 ## Usage
 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from ultralytics import YOLO
 
-model_id = 'AdithyaSK/cursor-detection-yolov8n'
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id)
+# Load model
+model = YOLO("AdithyaSK/cursor-detection-yolov8n/best.pt")
+
+# Detect cursor in an image
+results = model("screenshot.jpg")
+results[0].show()
 ```
 
-For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.
+## License
+
+AGPL-3.0 (same as Ultralytics YOLOv8)