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| ID | Class |
|---|---|
| 0 | stamp |
| 1 | signature |
| 2 | checkbox_checked |
| 3 | checkbox_unchecked |
| Class | mAP@50 | mAP@50:95 | Precision | Recall |
|---|---|---|---|---|
| stamp | 93.2% | 70.5% | 96.3% | 81% |
| signature | 95.4% | 73.9% | 96.6% | 81% |
| checkbox_checked | 91.8% | 56.5% | 90.7% | 81% |
| checkbox_unchecked | 65.7% | 34.2% | 43.4% | 81% |
| overall | 86.5% | 58.8% | 81.7% | 81% |
1from rfdetr import RFDETRBase
2from PIL import Image
3
4# Load model
5model = RFDETRBase()
6model.load("path/to/checkpoint_best_ema.pth")
7
8# Run inference
9image = Image.open("document.png")
10detections = model.predict(image, threshold=0.5)
11
12for det in detections:
13 print(f"Class: {det['class']}, Confidence: {det['confidence']:.2f}, Box: {det['bbox']}")@misc{bluecopa-stamp-detector,
title={RF-DETR Stamp/Signature Detector},
author={BlueCopa},
year={2024},
publisher={HuggingFace}
}