{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-morsetechlab--yolov11-license-plate-detection","slug":"morsetechlab--yolov11-license-plate-detection","name":"yolov11-license-plate-detection","type":"model","url":"https://huggingface.co/morsetechlab/yolov11-license-plate-detection","page_url":"https://unfragile.ai/morsetechlab--yolov11-license-plate-detection","categories":["image-generation"],"tags":["ultralytics","onnx","computer-vision","object-detection","license-plate","yolov11","finetuned","en","dataset:roboflow/license-plate-recognition-rxg4e","license:agpl-3.0","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-morsetechlab--yolov11-license-plate-detection__cap_0","uri":"capability://image.visual.real.time.license.plate.localization.in.images","name":"real-time license plate localization in images","description":"Detects and localizes license plates in images using YOLOv11's anchor-free detection architecture with convolutional feature pyramids. The model processes input images through a backbone network (CSPDarknet variant) that extracts multi-scale features, then applies detection heads to predict bounding box coordinates and confidence scores for license plate regions. Fine-tuned on the Roboflow license-plate-recognition-rxg4e dataset, it achieves spatial awareness of plate locations regardless of angle, lighting, or partial occlusion.","intents":["I need to automatically find where license plates are located in photos for downstream OCR processing","I want to build a traffic enforcement or parking management system that identifies vehicle plates in surveillance footage","I need to extract license plate regions from dashcam or security camera feeds for analysis"],"best_for":["computer vision engineers building end-to-end license plate recognition pipelines","teams developing traffic monitoring or parking enforcement systems","developers integrating plate detection into vehicle identification workflows"],"limitations":["Detection accuracy degrades significantly on heavily obscured, rotated (>45°), or motion-blurred plates","Model trained on specific dataset distribution — performance may vary on non-Latin character plates or unusual plate formats","Inference latency ~50-150ms per image on CPU; GPU acceleration required for real-time video processing (>30 FPS)","No built-in handling for multiple plates per image — returns all detected plates without ranking or filtering"],"requires":["Python 3.8+","ultralytics library (YOLOv11 implementation)","PyTorch 1.13+ or ONNX Runtime for inference","Input images minimum 320x320 pixels, optimal 640x640 or higher"],"input_types":["image (JPEG, PNG, BMP, WebP)","video frames (as sequential images)","numpy arrays (H×W×3 uint8 or float32)"],"output_types":["bounding boxes (x1, y1, x2, y2 coordinates)","confidence scores (0-1 float)","structured detection objects with metadata"],"categories":["image-visual","object-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-morsetechlab--yolov11-license-plate-detection__cap_1","uri":"capability://automation.workflow.multi.format.model.export.and.deployment","name":"multi-format model export and deployment","description":"Exports the YOLOv11 license plate detector to multiple inference formats including ONNX, TensorFlow SavedModel, CoreML, and TorchScript through Ultralytics' unified export pipeline. This enables deployment across heterogeneous environments: ONNX Runtime for CPU/GPU inference, CoreML for iOS/macOS edge devices, TensorFlow Lite for mobile, and native PyTorch for research. The export process applies quantization, pruning, and format-specific optimizations automatically.","intents":["I need to deploy this model to mobile devices (iOS/Android) with minimal latency","I want to run inference on edge devices or embedded systems without GPU access","I need to integrate the model into production systems using different ML frameworks (TensorFlow, ONNX Runtime, etc.)","I want to reduce model size and inference latency for real-time video processing on constrained hardware"],"best_for":["mobile and embedded systems engineers deploying vision models to edge devices","DevOps teams managing multi-platform ML inference infrastructure","developers building cross-platform applications requiring consistent model behavior"],"limitations":["Export to certain formats (CoreML, TensorFlow Lite) may lose precision or require post-export quantization tuning","ONNX export requires ONNX opset compatibility — older runtime versions may not support all operations","Quantized models (int8, float16) may show 2-5% accuracy degradation depending on target format","Export process itself requires GPU memory for larger batch sizes; CPU-only export is slower"],"requires":["ultralytics library with export support","Target framework installed (ONNX Runtime, TensorFlow, CoreML tools, etc.)","Python 3.8+","Original PyTorch model weights (.pt file)"],"input_types":["PyTorch model (.pt)","export format specification (string: 'onnx', 'tflite', 'coreml', 'torchscript', etc.)"],"output_types":["ONNX model (.onnx)","TensorFlow SavedModel (directory structure)","CoreML model (.mlmodel)","TorchScript (.pt)","TensorFlow Lite (.tflite)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-morsetechlab--yolov11-license-plate-detection__cap_2","uri":"capability://data.processing.analysis.batch.inference.with.configurable.confidence.thresholding","name":"batch inference with configurable confidence thresholding","description":"Processes multiple images or video frames in batches through the YOLOv11 detector with configurable confidence and IoU thresholds for filtering detections. The inference pipeline accepts variable-sized inputs, applies automatic padding/resizing, batches them for efficient GPU utilization, and returns detections filtered by user-specified confidence thresholds (default 0.25). Non-maximum suppression (NMS) with configurable IoU threshold (default 0.45) removes overlapping boxes, and results are returned as structured objects with bounding boxes, confidence scores, and class labels.","intents":["I need to process hundreds of images efficiently and extract license plate locations with minimal false positives","I want to tune detection sensitivity — reduce false positives by raising confidence threshold or catch more plates by lowering it","I need to process video streams frame-by-frame and get consistent, deduplicated detections","I want to benchmark model performance on a dataset and need batch processing for speed"],"best_for":["data scientists evaluating model performance on large image datasets","production systems processing video streams or image batches at scale","developers fine-tuning detection sensitivity for specific use cases (e.g., high-precision enforcement vs. broad surveillance)"],"limitations":["Batch processing requires sufficient GPU memory — batch size must be reduced for large images or limited VRAM","Confidence threshold tuning is manual; no automated threshold optimization provided","NMS operates independently per image — no temporal consistency across video frames without additional post-processing","Variable input sizes within a batch require padding, which can introduce artifacts at image boundaries"],"requires":["ultralytics YOLOv11 model loaded in memory","PyTorch or ONNX Runtime installed","GPU recommended for batch processing (CPU inference is 5-10x slower)","Images in standard formats (JPEG, PNG, etc.) or numpy arrays"],"input_types":["list of image paths (strings)","list of numpy arrays (H×W×3)","video file path (string)","confidence threshold (float, 0-1)","IoU threshold (float, 0-1)"],"output_types":["list of detection objects per image","bounding boxes (x1, y1, x2, y2)","confidence scores (float)","class labels (integer or string)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-morsetechlab--yolov11-license-plate-detection__cap_3","uri":"capability://code.generation.editing.fine.tuning.on.custom.license.plate.datasets","name":"fine-tuning on custom license plate datasets","description":"Supports transfer learning by fine-tuning the pre-trained YOLOv11 license plate detector on custom annotated datasets using Ultralytics' training pipeline. The process loads pre-trained weights, freezes early backbone layers, and trains detection heads on new data with configurable hyperparameters (learning rate, augmentation, epochs). Training includes data augmentation (mosaic, mixup, HSV jitter, rotation), automatic validation on a held-out set, and metric tracking (mAP, precision, recall). The model converges faster than training from scratch due to feature reuse from the original license plate dataset.","intents":["I need to adapt this model to detect license plates in a specific region or country with different plate formats","I want to improve detection accuracy on my proprietary dataset of vehicle images","I need to fine-tune the model to handle specific conditions (night vision, low resolution, unusual angles) relevant to my use case","I want to reduce false positives by training on negative examples specific to my deployment environment"],"best_for":["computer vision teams with labeled datasets wanting to customize the model for domain-specific plate formats","organizations deploying in regions with non-standard license plate designs","developers optimizing model performance for specific environmental conditions (lighting, camera angles, weather)"],"limitations":["Requires properly annotated dataset in YOLO format (bounding box coordinates) — annotation effort is significant for large datasets","Fine-tuning on small datasets (<500 images) risks overfitting; minimum ~1000 images recommended for stable convergence","Training time varies with dataset size and hardware — GPU required for practical training (CPU training is prohibitively slow)","Hyperparameter tuning (learning rate, augmentation intensity) is manual; no automated hyperparameter optimization included","Fine-tuned model may lose generalization to the original Roboflow dataset if trained on very different plate styles"],"requires":["ultralytics library with training support","PyTorch 1.13+ with CUDA support (GPU strongly recommended)","Annotated dataset in YOLO format (images + .txt files with bounding box coordinates)","Python 3.8+","Sufficient GPU memory (8GB+ recommended for batch size 16)"],"input_types":["dataset directory structure (images/ and labels/ folders)","YAML configuration file specifying dataset paths and class names","hyperparameter overrides (learning rate, epochs, batch size, augmentation settings)"],"output_types":["fine-tuned model weights (.pt file)","training metrics (loss curves, mAP, precision, recall)","validation results on held-out test set","best model checkpoint (automatically selected by validation mAP)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-morsetechlab--yolov11-license-plate-detection__cap_4","uri":"capability://automation.workflow.onnx.based.cross.platform.inference.without.framework.dependencies","name":"onnx-based cross-platform inference without framework dependencies","description":"Enables inference using ONNX Runtime, a lightweight inference engine that runs the exported ONNX model without requiring PyTorch, TensorFlow, or other deep learning frameworks. ONNX Runtime optimizes execution across CPUs, GPUs, and specialized accelerators (NPU, TPU) through provider-based execution. The model runs identically across Windows, Linux, macOS, and embedded systems, making it ideal for production deployments where minimizing dependencies and ensuring consistency are critical. Inference latency is typically 10-20% faster than PyTorch due to graph optimization and operator fusion.","intents":["I need to deploy the license plate detector in production without installing heavy ML frameworks","I want to ensure the model behaves identically across different operating systems and hardware","I need to minimize container size and startup time for serverless or containerized deployments","I want to run inference on specialized hardware (NPU, TPU) with automatic provider selection"],"best_for":["production DevOps teams deploying models in containerized or serverless environments","embedded systems engineers with strict dependency and size constraints","organizations requiring cross-platform model consistency and reproducibility"],"limitations":["ONNX Runtime requires the model to be pre-exported to ONNX format — cannot directly load PyTorch .pt files","Some custom operations or dynamic control flow in the original model may not be supported in ONNX; standard YOLOv11 is fully compatible","Provider selection (CPU, GPU, NPU) must be configured explicitly; automatic fallback requires error handling","Debugging ONNX model issues is harder than PyTorch due to less visibility into intermediate activations"],"requires":["ONNX model file (.onnx) exported from the YOLOv11 detector","ONNX Runtime library (pip install onnxruntime)","Python 3.7+ or C++/C# bindings for other languages","Optional: GPU provider (onnxruntime-gpu) for CUDA/TensorRT acceleration"],"input_types":["ONNX model file (.onnx)","input image as numpy array (H×W×3, uint8 or float32)","provider specification ('CPUExecutionProvider', 'CUDAExecutionProvider', 'TensorrtExecutionProvider', etc.)"],"output_types":["detection outputs (bounding boxes, confidence scores, class IDs)","numpy arrays matching YOLOv11 output format","structured detection objects (via post-processing wrapper)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","ultralytics library (YOLOv11 implementation)","PyTorch 1.13+ or ONNX Runtime for inference","Input images minimum 320x320 pixels, optimal 640x640 or higher","ultralytics library with export support","Target framework installed (ONNX Runtime, TensorFlow, CoreML tools, etc.)","Original PyTorch model weights (.pt file)","ultralytics YOLOv11 model loaded in memory","PyTorch or ONNX Runtime installed","GPU recommended for batch processing (CPU inference is 5-10x slower)"],"failure_modes":["Detection accuracy degrades significantly on heavily obscured, rotated (>45°), or motion-blurred plates","Model trained on specific dataset distribution — performance may vary on non-Latin character plates or unusual plate formats","Inference latency ~50-150ms per image on CPU; GPU acceleration required for real-time video processing (>30 FPS)","No built-in handling for multiple plates per image — returns all detected plates without ranking or filtering","Export to certain formats (CoreML, TensorFlow Lite) may lose precision or require post-export quantization tuning","ONNX export requires ONNX opset compatibility — older runtime versions may not support all operations","Quantized models (int8, float16) may show 2-5% accuracy degradation depending on target format","Export process itself requires GPU memory for larger batch sizes; CPU-only export is slower","Batch processing requires sufficient GPU memory — batch size must be reduced for large images or limited VRAM","Confidence threshold tuning is manual; no automated threshold optimization provided","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4287279953491353,"quality":0.35,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:58.552Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":26512,"model_likes":33}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=morsetechlab--yolov11-license-plate-detection","compare_url":"https://unfragile.ai/compare?artifact=morsetechlab--yolov11-license-plate-detection"}},"signature":"Nj32LlBB9s661ORowdvvsO9LwOSgHyo/sl8LInhj3jGPrwUUTu1wxiLyRYvB2XtYZPhMyzl0PqMVAJ0MduICBg==","signedAt":"2026-06-23T00:36:18.119Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/morsetechlab--yolov11-license-plate-detection","artifact":"https://unfragile.ai/morsetechlab--yolov11-license-plate-detection","verify":"https://unfragile.ai/api/v1/verify?slug=morsetechlab--yolov11-license-plate-detection","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}