SanderGi commited on
Commit
eac8396
·
verified ·
1 Parent(s): 73a5975

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +40 -3
README.md CHANGED
@@ -1,3 +1,40 @@
1
- ---
2
- license: mit
3
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ base_model:
4
+ - Ultralytics/YOLO11
5
+ pipeline_tag: object-detection
6
+ tags:
7
+ - printed-circuit-boards
8
+ library_name: ultralytics
9
+ metrics:
10
+ - f1 - 93.8%
11
+ - mAP50 - 93.0%
12
+ ---
13
+
14
+ # PCB Detection
15
+
16
+ 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.
17
+
18
+ ## Usage
19
+
20
+ 1. Download [`the model weights`](https://huggingface.co/SanderGi/PCB-OBB/resolve/main/best.pt?download=true)
21
+ 2. `pip install ultralytics`
22
+ 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:
23
+
24
+ ```python
25
+ from ultralytics import YOLO
26
+
27
+ model = YOLO('[path to model weights]')
28
+ results = model.predict('[path/to/test/image.jpg]')
29
+ ```
30
+
31
+ ## Results
32
+
33
+ Dataset | Precision | Recall | F1 Score | mAP50 | mAP50-95
34
+ -----------|-----------|--------|----------|--------|---------
35
+ Training | 100.0% | 100.0% | 100.0% | 100.0% | 100.0%
36
+ Validation | 100.0% | 100.0% | 100.0% | 99.5% | 97.0%
37
+ Test | 100.0% | 88.4% | 93.8% | 93.0% | 91.2%
38
+
39
+ Sample predictions:
40
+ ![sample predictions](https://github.com/SanderGi/PCB-Detection/raw/refs/heads/main/data/augmented_obb/runs/no_perspective3/val_batch1_pred.jpg)