Update README.md

README.md

@@ -1,3 +1,60 @@
----
-license: mit
----
+---
+license: mit
+base_model:
+- Ultralytics/YOLO11
+tags:
+- printed-circuit-boards
+library_name: ultralytics
+model-index:
+- name: ultralytics/yolo11
+  results:
+  - task:
+      type: image-segmentation
+    metrics:
+    - type: f1
+      value: 99.8%
+      name: F1 Score
+    - type: mAP50
+      value: 99.5%
+      name: mAP50
+metrics:
+- f1 - 99.8%
+- mAP50 - 99.5%
+---
+# 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.
+
+Read more [here](https://github.com/SanderGi/PCB-Detection).
+
+## Usage
+
+1. Download [`the model weights`](https://huggingface.co/SanderGi/PCB-SEG/resolve/main/best.pt?download=true)
+2. `pip install ultralytics`
+3. Run the model with `yolo task=segment 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
+
+### Segmentation
+Dataset    | Precision | Recall | F1 Score | mAP50 | mAP50-95
+-----------|-----------|--------|----------|-------|---------
+Training   | 100.0%    | 23.2%  | 37.7%    | 39.4% | 39.1%
+Validation | 99.9%     | 39.6%  | 56.7%    | 51.7% | 51.0%
+Test       | 99.7%     | 100%   | 99.8%    | 99.5% | 95.6%
+
+Sample predictions:
+![sample predictions](https://github.com/SanderGi/PCB-Detection/raw/refs/heads/main/data//augmented_seg/runs/no_perspective/val_batch1_pred.jpg)
+
+### Object Detection*
+
+Dataset    | Precision | Recall | F1 Score | mAP50 | mAP50-95
+-----------|-----------|--------|----------|-------|---------
+Training   | 100.0%    | 23.2%  | 37.7%    | 39.4% | 39.3%
+Validation | 99.9%     | 39.6%  | 56.7%    | 51.7% | 51.3%
+Test       | 99.7%     | 100%   | 99.8%    | 99.5% | 94.5%