Instructions to use thangkt/PCB-Prune-YOLO-P30-Direct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use thangkt/PCB-Prune-YOLO-P30-Direct with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("thangkt/PCB-Prune-YOLO-P30-Direct") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- validation/metrics_val.csv +8 -0
- validation/metrics_val.json +59 -0
validation/metrics_val.csv
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scope,precision,recall,mAP50,mAP50-95,class_id,class_name
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overall,0.9532359026085936,0.9437431976398601,0.9778818888829804,0.7503037352615889,,
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class,0.9444039780554989,0.9604743083003953,0.9793098515053684,0.6819127362573131,0.0,open
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class,0.9326621433423198,0.9191919191919192,0.9562128393802523,0.6617615928672392,1.0,short
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class,0.9320049579136033,0.9170176634934548,0.9711807173070884,0.7322388357813507,2.0,mousebite
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class,0.9655674315697388,0.9523809523809523,0.9806989344610584,0.7097311791921733,3.0,spur
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class,0.975695738368658,0.9513090344155675,0.9922288733289836,0.8797282879332101,4.0,copper
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class,0.9690811664017428,0.9620853080568721,0.9876601173151319,0.8364497795382461,5.0,pin-hole
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validation/metrics_val.json
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{
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"split": "val",
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"overall": {
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"precision": 0.9532359026085936,
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"recall": 0.9437431976398601,
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"mAP50": 0.9778818888829804,
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"mAP50-95": 0.7503037352615889
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},
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"per_class": [
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{
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"class_id": 0,
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"class_name": "open",
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"precision": 0.9444039780554989,
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"recall": 0.9604743083003953,
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"mAP50": 0.9793098515053684,
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"mAP50-95": 0.6819127362573131
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},
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{
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"class_id": 1,
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"class_name": "short",
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"precision": 0.9326621433423198,
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"recall": 0.9191919191919192,
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"mAP50": 0.9562128393802523,
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"mAP50-95": 0.6617615928672392
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},
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{
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"class_id": 2,
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"class_name": "mousebite",
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"precision": 0.9320049579136033,
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"recall": 0.9170176634934548,
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"mAP50": 0.9711807173070884,
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"mAP50-95": 0.7322388357813507
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},
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{
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"class_id": 3,
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"class_name": "spur",
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"precision": 0.9655674315697388,
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"recall": 0.9523809523809523,
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"mAP50": 0.9806989344610584,
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"mAP50-95": 0.7097311791921733
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},
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{
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"class_id": 4,
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"class_name": "copper",
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"precision": 0.975695738368658,
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"recall": 0.9513090344155675,
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"mAP50": 0.9922288733289836,
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"mAP50-95": 0.8797282879332101
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},
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{
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"class_id": 5,
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"class_name": "pin-hole",
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"precision": 0.9690811664017428,
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"recall": 0.9620853080568721,
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"mAP50": 0.9876601173151319,
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"mAP50-95": 0.8364497795382461
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}
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]
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}
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