Update README.md

README.md

@@ -1,3 +1,40 @@
----
-license: mit
----
+---
+license: mit
+base_model:
+- Ultralytics/YOLO11
+pipeline_tag: object-detection
+tags:
+- printed-circuit-boards
+library_name: ultralytics
+metrics:
+- f1 - 93.8%
+- mAP50 - 93.0%
+---
+
+# PCB Detection
+
+There are [a lot of models](https://universe.roboflow.com/roboflow-100/printed-circuit-board/model/3) for detecting components within a Printed Circuit Board (PCB), but not as many for detecting which pixels (if any) in an image contain the PCB itself. Being able to determine if and where a PCB is in an image is useful for [calculating its size to estimate carbon footprint]((https://github.com/SanderGi/LCA)), as a preprocessing step for detecting components, to limit the amount of image more expensive PCB defect detection models have to process, and more.
+
+## Usage
+
+1. Download [`the model weights`](https://huggingface.co/SanderGi/PCB-OBB/resolve/main/best.pt?download=true)
+2. `pip install ultralytics`
+3. Run the model with `yolo task=obb mode=predict model=[path to model weights] source=[path to test image]` from the terminal or with Python:
+
+```python
+from ultralytics import YOLO
+
+model = YOLO('[path to model weights]')
+results = model.predict('[path/to/test/image.jpg]')
+```
+
+## Results
+
+Dataset    | Precision | Recall | F1 Score | mAP50  | mAP50-95
+-----------|-----------|--------|----------|--------|---------
+Training   | 100.0%    | 100.0% | 100.0%   | 100.0% | 100.0%
+Validation | 100.0%    | 100.0% | 100.0%   | 99.5%  | 97.0%
+Test       | 100.0%    | 88.4%  | 93.8%    | 93.0%  | 91.2%
+
+Sample predictions:
+![sample predictions](https://github.com/SanderGi/PCB-Detection/raw/refs/heads/main/data/augmented_obb/runs/no_perspective3/val_batch1_pred.jpg)