License Plate Keypoint Detection

--- license: apache-2.0 tags: - object-detection - keypoint-detection - license-plate - yolov8 - pose-estimation - computer-vision - ultralytics datasets: - roboflow model-index: - name: license-plate-keypoint-detection results: - task: type: keypoint-detection metrics: - type: mAP value: 0.9875 name: "Box mAP@50" - type: mAP value: 0.8412 name: "Box mAP@50-95" - type: mAP value: 0.9264 name: "Pose mAP@50" - type: mAP value: 0.9137 name: "Pose mAP@50-95" language: - en pipeline_tag: object-detection --- # License Plate Keypoint Detection A fine-tuned **YOLOv8m-Pose** model that detects vehicle license plates and precisely localizes their **4 corner keypoints** (top-left, top-right, bottom-right, bottom-left). Designed for downstream tasks like logo replacement, plate anonymization, and perspective-corrected OCR. --- ## Model Details | Property | Value | |---|---| | **Base Model** | YOLOv8m-Pose (Ultralytics) | | **Task** | Object Detection + Keypoint Estimation | | **Classes** | 1 (`Plate`) | | **Keypoints** | 4 corners per plate (TL, TR, BR, BL) | | **Input Size** | 640 × 640 px | | **Model Size** | ~101 MB | | **Framework** | PyTorch / Ultralytics | --- ## Performance Metrics Evaluated on the held-out validation set (24 images): | Metric | Value | |---|---| | Box Precision | 0.977 | | Box Recall | 0.947 | | **Box mAP@50** | **0.9875** | | Box mAP@50-95 | 0.8412 | | Pose Precision | 0.934 | | Pose Recall | 0.898 | | **Pose mAP@50** | **0.9264** | | Pose mAP@50-95 | 0.9137 | > Model fully trained for all 150 epochs, achieving best-in-class performance at the final checkpoint. --- ## Training Details | Parameter | Value | |---|---| | Epochs | 150 | | Batch Size | 16 | | Image Size | 640 × 640 | | Optimizer | SGD (auto) | | Learning Rate | 0.01 → 0.01 | | Momentum | 0.937 | | Weight Decay | 0.0005 | | Pose Loss Weight | 12.0 | | Keypoint Object Loss | 2.0 | | Warmup Epochs | 3 | | Augmentations | Mosaic, HSV, Flip-LR, Scale, Shear, Rotation | | Device | CUDA GPU | ### Dataset - **Source**: Roboflow — `license-plate-new` dataset (v3) - **License**: CC BY 4.0 - **Train**: 19000 images - **Validation**: 2400 images - **Test**: 2400 images - **Annotation**: YOLO Pose format, 4 keypoints per plate (x, y, visibility) --- ## Quick Start ### Installation ```bash pip install ultralytics opencv-python numpy ``` ### Basic Inference ```python from ultralytics import YOLO import cv2 # Load model model = YOLO("license_plate_keypoint.pt") # Run inference on an image results = model("car.jpg", conf=0.25) for result in results: if result.keypoints is not None: for kpts in result.keypoints: # kpts.xy shape: [4, 2] — (x, y) for each of the 4 corners corners = kpts.xy[0].cpu().numpy() print("TL:", corners[0]) print("TR:", corners[1]) print("BR:", corners[2]) print("BL:", corners[3]) ``` ### Using the Full Inference Pipeline ```python from inference import LicensePlateKeypointDetector detector = LicensePlateKeypointDetector("license_plate_keypoint.pt") # Detect keypoints only result = detector.detect("car.jpg") if result["success"]: print("Keypoints:", result["keypoints"]) print("Confidence:", result["confidence"]) # Detect and blur the plate blurred = detector.blur_plate("car.jpg", output_path="blurred.jpg") # Detect and replace with a logo replaced = detector.replace_logo("car.jpg", "logo.png", output_path="out.jpg") ``` ### Batch Processing ```python from inference import LicensePlateKeypointDetector from pathlib import Path detector = LicensePlateKeypointDetector("license_plate_keypoint.pt") for img_path in Path("input_images").glob("*.jpg"): result = detector.detect(str(img_path)) if result["success"]: print(f"{img_path.name}: {result['keypoints']}") ``` ### Video Processing ```python from inference import LicensePlateKeypointDetector detector = LicensePlateKeypointDetector("license_plate_keypoint.pt") detector.process_video("input.mp4", output_path="output.mp4", blur=True) ``` --- ## Keypoint Order The model outputs **4 keypoints per detected plate** in this fixed order: ``` 0: Top-Left (TL) 1: Top-Right (TR) 2: Bottom-Right (BR) 3: Bottom-Left (BL) ``` Each keypoint has `(x, y, visibility)` values. Visibility > 0.5 means the point is reliably detected. --- ## Use Cases - **Plate Anonymization** — Blur or mask license plates for privacy compliance - **Logo Replacement** — Replace plates with custom logos using perspective transform - **OCR Pre-processing** — Extract and warp plate regions for accurate text recognition - **Dataset Annotation** — Auto-annotate corner keypoints for further training - **Traffic Monitoring** — Track vehicle plates in video streams --- ## Limitations - Trained primarily on clear, front-facing license plate images - Performance may degrade on heavily occluded, night-time, or extreme-angle plates - Best results at image resolution ≥ 640px - Optimized for standard rectangular license plates --- ## Files | File | Description | |---|---| | `license_plate_keypoint.pt` | Fine-tuned YOLOv8m-Pose model weights (~101 MB) | | `inference.py` | Full inference pipeline with blur, logo replacement, video support | | `requirements.txt` | Python dependencies | | `config.json` | Default inference configuration | --- ## Citation If you use this model, please cite: ```bibtex @misc{prasanna2024licenseplatepose, title = {License Plate Keypoint Detection using YOLOv8 Pose}, author = {Prasanna B}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/PrasannaBAImodel/license-plate-keypoint-detection} } ``` --- ## License This model is released under the **Apache 2.0 License**. Base model (YOLOv8) is © Ultralytics, released under AGPL-3.0. Training data from Roboflow under CC BY 4.0.