{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-keremberke--yolov5m-license-plate","slug":"keremberke--yolov5m-license-plate","name":"yolov5m-license-plate","type":"model","url":"https://huggingface.co/keremberke/yolov5m-license-plate","page_url":"https://unfragile.ai/keremberke--yolov5m-license-plate","categories":["image-generation"],"tags":["yolov5","tensorboard","yolo","vision","object-detection","pytorch","dataset:keremberke/license-plate-object-detection","model-index","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-keremberke--yolov5m-license-plate__cap_0","uri":"capability://image.visual.real.time.license.plate.detection.in.images","name":"real-time license plate detection in images","description":"Detects and localizes license plates in images using YOLOv5m architecture, which employs a single-stage convolutional neural network with multi-scale feature pyramid for efficient bounding box regression and confidence scoring. The model processes images through a backbone (CSPDarknet), neck (PANet), and head (detection layers) to output bounding box coordinates, confidence scores, and class predictions in a single forward pass without region proposal generation.","intents":["I need to automatically locate license plates in surveillance or traffic camera footage","I want to extract license plate regions from images for downstream OCR processing","I need to build a vehicle monitoring system that identifies where plates are located before reading them","I want to filter or redact license plates in bulk image datasets for privacy compliance"],"best_for":["computer vision engineers building traffic enforcement or parking management systems","developers creating privacy-preserving image processing pipelines","teams deploying edge-based vehicle identification systems on resource-constrained hardware"],"limitations":["Optimized for standard front-facing license plates; performance degrades on severely angled, obscured, or non-Latin character plates","YOLOv5m is a medium-weight model (~40MB); larger datasets or real-time 4K video require GPU acceleration or model quantization","No built-in handling for multiple license plates per image beyond standard NMS (non-maximum suppression) — overlapping detections may be suppressed","Trained on keremberke/license-plate-object-detection dataset; generalization to regional plate formats outside training distribution is untested"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+ for inference","Python 3.7+","PIL/Pillow for image loading and preprocessing","GPU (CUDA 11.0+) recommended for batch processing; CPU inference possible but ~5-10x slower","Minimum 512MB RAM for model weights and inference buffers"],"input_types":["image (JPEG, PNG, BMP, WebP)","image batch (multiple images for parallel processing)","video frames (as individual images or via frame extraction)"],"output_types":["bounding boxes (x_min, y_min, x_max, y_max coordinates)","confidence scores (0.0-1.0 per detection)","class labels (license plate)","structured JSON with detection metadata"],"categories":["image-visual","object-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-keremberke--yolov5m-license-plate__cap_1","uri":"capability://image.visual.batch.license.plate.detection.with.confidence.filtering","name":"batch license plate detection with confidence filtering","description":"Processes multiple images sequentially or in parallel batches through the YOLOv5m detector, applying configurable confidence thresholds and non-maximum suppression (NMS) to filter low-confidence detections and remove overlapping bounding boxes. Outputs structured results per image with optional filtering by detection confidence, enabling downstream filtering of uncertain predictions before OCR or database storage.","intents":["I need to process 1000s of images from a traffic camera archive and extract only high-confidence plate detections","I want to tune detection sensitivity to reduce false positives in my plate recognition pipeline","I need to batch-process video frames and extract plate regions with quality control","I want to generate a dataset of detected plates with confidence scores for model evaluation"],"best_for":["data engineers building ETL pipelines for traffic or parking datasets","ML teams evaluating model performance across large image collections","production systems requiring configurable precision-recall tradeoffs"],"limitations":["Batch processing speed is I/O-bound on CPU; GPU batch processing requires careful memory management (batch size tuning for VRAM constraints)","NMS is applied globally per image; no cross-image temporal consistency for video sequences","Confidence threshold tuning requires manual validation on representative data; no automatic threshold optimization","Output format varies by inference framework (Ultralytics, TensorFlow, ONNX); no unified serialization standard"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+ or ONNX Runtime 1.10+","Ultralytics YOLOv5 library (pip install yolov5) OR direct model loading via torch.hub","Sufficient disk I/O bandwidth for image loading (SSD recommended for 1000+ images)","GPU with 2GB+ VRAM for batch inference (optional but recommended)"],"input_types":["image directory (glob patterns supported)","image list (CSV, JSON with file paths)","video file (requires frame extraction preprocessing)","image batch (in-memory arrays)"],"output_types":["JSON Lines (one detection result per line)","CSV (image_path, x_min, y_min, x_max, y_max, confidence)","annotated images (visualization with bounding boxes)","cropped plate regions (extracted sub-images)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-keremberke--yolov5m-license-plate__cap_2","uri":"capability://image.visual.license.plate.region.extraction.and.cropping","name":"license plate region extraction and cropping","description":"Extracts detected license plate regions from source images by computing bounding box coordinates and cropping the original image to isolate the plate area. Supports padding/margin expansion around detected boxes for downstream OCR preprocessing, and can apply optional image normalization (resizing, contrast enhancement) to standardize plate regions for character recognition models.","intents":["I need to extract just the license plate region from a full image for OCR processing","I want to create a dataset of cropped plates with consistent sizing for training an OCR model","I need to add padding around detected plates to ensure OCR models capture full character boundaries","I want to normalize extracted plates (resize, enhance contrast) before feeding to downstream recognition systems"],"best_for":["developers building end-to-end license plate recognition pipelines (detection → OCR)","teams creating training datasets for plate character recognition models","systems requiring standardized plate region inputs for legacy OCR engines"],"limitations":["Cropping quality depends on detection accuracy; misaligned bounding boxes produce incomplete or noisy plate regions","No automatic rotation correction for tilted plates; requires additional preprocessing if plates are at angles","Padding expansion can exceed image boundaries on edge-detected plates; requires boundary clamping logic","Resizing small detected regions to standard sizes (e.g., 224x224) may introduce artifacts or blur fine character details"],"requires":["Python 3.7+","PIL/Pillow 8.0+ or OpenCV 4.5+ for image cropping and resizing","NumPy 1.19+ for coordinate manipulation","Bounding box coordinates from license plate detection (x_min, y_min, x_max, y_max)"],"input_types":["image (JPEG, PNG, BMP, WebP)","bounding box coordinates (x_min, y_min, x_max, y_max)","padding/margin specification (pixels or percentage)"],"output_types":["cropped image (JPEG, PNG)","resized plate region (standardized dimensions)","normalized plate array (NumPy array for model input)","metadata (original coordinates, crop dimensions, padding applied)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-keremberke--yolov5m-license-plate__cap_3","uri":"capability://image.visual.multi.format.model.inference.pytorch.tensorflow.onnx","name":"multi-format model inference (pytorch, tensorflow, onnx)","description":"Provides inference compatibility across multiple deep learning frameworks through model export and runtime abstraction. The YOLOv5m model can be loaded and executed via PyTorch (native), TensorFlow (converted weights), or ONNX Runtime (optimized for production), enabling deployment flexibility across different hardware and software stacks without retraining or architecture changes.","intents":["I need to deploy the license plate detector on a server using TensorFlow instead of PyTorch","I want to run inference on edge devices using ONNX Runtime for better performance","I need to integrate the model into a C++ application that doesn't support PyTorch","I want to optimize inference latency by converting to ONNX and quantizing for mobile deployment"],"best_for":["DevOps engineers deploying models across heterogeneous infrastructure","embedded systems developers targeting edge devices (Jetson, Raspberry Pi, mobile)","teams requiring framework-agnostic model serving (TensorFlow Serving, ONNX Runtime Server)","organizations with existing TensorFlow or ONNX pipelines needing to integrate YOLOv5"],"limitations":["Model conversion (PyTorch → TensorFlow/ONNX) may introduce numerical precision differences; requires validation on test data","ONNX export requires opset version compatibility; older ONNX Runtime versions may not support all operations","TensorFlow conversion adds ~500MB disk space overhead; ONNX quantization requires calibration dataset","Framework-specific optimizations (e.g., PyTorch JIT tracing) are lost in conversion; inference latency varies by framework"],"requires":["PyTorch 1.9+ (for native inference or export)","TensorFlow 2.4+ (for TensorFlow inference)","ONNX 1.10+ and ONNX Runtime 1.10+ (for ONNX inference)","Model weights file (yolov5m.pt or converted variants)","Framework-specific dependencies (torch, tensorflow, onnxruntime)"],"input_types":["image (JPEG, PNG, BMP, WebP)","image batch (tensor or array)","model weights (PyTorch .pt, TensorFlow SavedModel, ONNX .onnx)"],"output_types":["detection tensors (bounding boxes, confidence scores, class labels)","framework-specific outputs (torch.Tensor, tf.Tensor, NumPy array)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-keremberke--yolov5m-license-plate__cap_4","uri":"capability://image.visual.model.quantization.and.optimization.for.edge.deployment","name":"model quantization and optimization for edge deployment","description":"Reduces model size and inference latency through quantization techniques (INT8, FP16) and pruning, enabling deployment on resource-constrained devices (mobile, embedded, IoT). YOLOv5m can be quantized to ~10MB (from ~40MB) with minimal accuracy loss, and inference latency improves 2-4x on edge hardware (Jetson Nano, Raspberry Pi) through framework-specific optimizations (TensorRT, CoreML, OpenVINO).","intents":["I need to deploy the license plate detector on a Jetson Nano with limited VRAM and storage","I want to run inference on a Raspberry Pi 4 for a local traffic monitoring system","I need to reduce model size from 40MB to <15MB for mobile app deployment","I want to optimize inference latency to <50ms per image on edge devices"],"best_for":["embedded systems engineers deploying vision models on IoT devices","mobile app developers integrating object detection without cloud dependencies","edge computing teams optimizing for bandwidth and latency constraints","cost-sensitive deployments requiring minimal hardware (Raspberry Pi, Jetson Nano)"],"limitations":["INT8 quantization may reduce mAP by 1-3% depending on calibration data quality; requires validation on representative images","Quantized models are framework-specific (TensorRT for NVIDIA, CoreML for Apple); no universal quantized format","Edge devices have limited RAM; batch inference is constrained to batch_size=1 or small batches","Quantization requires calibration dataset; generic calibration may underperform on domain-specific plates (e.g., regional formats)"],"requires":["NVIDIA TensorRT 8.0+ (for Jetson devices) OR CoreML tools (for Apple) OR OpenVINO toolkit (for Intel)","Calibration dataset (100-500 representative images for INT8 quantization)","Target hardware specifications (VRAM, storage, compute capability)","Python 3.7+ with quantization framework (torch-quantization, TensorFlow Lite, ONNX quantization tools)"],"input_types":["pre-trained YOLOv5m model (PyTorch .pt or ONNX .onnx)","calibration dataset (images for quantization calibration)","quantization configuration (bit-width, calibration method)"],"output_types":["quantized model (TensorRT .engine, CoreML .mlmodel, ONNX quantized .onnx)","quantization report (accuracy loss, latency improvement, model size reduction)","deployment bundle (model + runtime dependencies)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-keremberke--yolov5m-license-plate__cap_5","uri":"capability://data.processing.analysis.confidence.based.detection.filtering.and.post.processing","name":"confidence-based detection filtering and post-processing","description":"Applies configurable confidence thresholds and non-maximum suppression (NMS) to filter low-confidence detections and remove overlapping bounding boxes. The model outputs raw predictions (bounding boxes, confidence scores) which are post-processed using NMS with IoU (Intersection over Union) threshold to eliminate duplicate detections and retain only high-confidence plates, enabling precision-recall tradeoff tuning.","intents":["I want to filter out low-confidence detections to reduce false positives in my plate recognition system","I need to tune detection sensitivity for different use cases (e.g., strict enforcement vs. informational monitoring)","I want to remove overlapping bounding boxes when multiple detections overlap the same plate","I need to generate confidence-weighted detection results for downstream ranking or filtering"],"best_for":["ML engineers tuning model performance for production deployments","teams requiring configurable precision-recall tradeoffs for different applications","systems with downstream processing that requires high-confidence inputs (e.g., OCR with low error tolerance)"],"limitations":["Confidence threshold tuning is manual and dataset-dependent; no automatic optimization without labeled validation data","NMS is applied per-image; no temporal consistency across video frames (consecutive frames may have inconsistent detections)","Threshold values (conf, iou) are global; no per-class or per-region adaptive thresholding","Post-processing adds ~5-10ms latency per image; not negligible for real-time systems requiring <30ms total latency"],"requires":["YOLOv5m model inference output (raw predictions with confidence scores)","Configurable confidence threshold (default 0.25, typical range 0.1-0.9)","NMS IoU threshold (default 0.45, typical range 0.3-0.7)","Python 3.7+ with NumPy for post-processing"],"input_types":["raw model predictions (bounding boxes, confidence scores, class labels)","confidence threshold (float 0.0-1.0)","NMS IoU threshold (float 0.0-1.0)"],"output_types":["filtered detections (bounding boxes, confidence scores above threshold)","NMS-deduplicated results (non-overlapping bounding boxes)","detection metadata (confidence distribution, number of detections before/after filtering)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-keremberke--yolov5m-license-plate__cap_6","uri":"capability://data.processing.analysis.model.performance.evaluation.and.metrics.computation","name":"model performance evaluation and metrics computation","description":"Computes standard object detection metrics (mAP, precision, recall, F1-score) by comparing predicted bounding boxes against ground truth annotations using IoU-based matching. Supports evaluation on validation/test datasets with detailed per-class metrics, confusion matrices, and visualization of detection performance across confidence thresholds, enabling quantitative assessment of model accuracy on license plate detection tasks.","intents":["I need to evaluate the pre-trained model's performance on my own license plate dataset","I want to compute mAP@0.5 and mAP@0.5:0.95 to compare against published benchmarks","I need to generate precision-recall curves to understand model behavior at different confidence thresholds","I want to identify failure cases (false positives, false negatives) to guide data collection or fine-tuning"],"best_for":["ML engineers validating model performance before production deployment","researchers benchmarking license plate detection models","teams evaluating whether pre-trained models meet accuracy requirements for their use case"],"limitations":["Evaluation requires ground truth annotations (COCO or YOLO format); unlabeled datasets cannot be evaluated","Metrics are sensitive to IoU threshold choice (mAP@0.5 vs mAP@0.5:0.95); different thresholds yield different conclusions","Per-class metrics are limited to 'license-plate' class; no breakdown by plate type, region, or condition","Evaluation is computationally expensive on large datasets (1000+ images); requires GPU for reasonable runtime"],"requires":["Ground truth annotations in COCO JSON or YOLO TXT format","Validation/test dataset with images and corresponding annotations","Python 3.7+ with evaluation libraries (pycocotools, or Ultralytics' built-in evaluation)","GPU recommended for large-scale evaluation (optional but improves speed)"],"input_types":["predicted detections (bounding boxes, confidence scores)","ground truth annotations (bounding boxes, class labels)","dataset split (validation or test set)"],"output_types":["mAP (mean Average Precision) at different IoU thresholds","precision, recall, F1-score per confidence threshold","confusion matrix (TP, FP, FN counts)","precision-recall curve (visualization)","per-image evaluation results (detection matches, unmatched predictions/annotations)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-keremberke--yolov5m-license-plate__cap_7","uri":"capability://image.visual.transfer.learning.and.fine.tuning.on.custom.license.plate.datasets","name":"transfer learning and fine-tuning on custom license plate datasets","description":"Enables fine-tuning the pre-trained YOLOv5m model on custom license plate datasets by leveraging transfer learning. The model's backbone and neck are pre-trained on general object detection; only the detection head is retrained on domain-specific plate data, reducing training time and data requirements compared to training from scratch. Supports data augmentation (mosaic, mixup, rotation) and hyperparameter tuning for improved convergence on custom datasets.","intents":["I have a custom dataset of license plates from my region and want to fine-tune the model for better accuracy","I need to adapt the model to detect plates with different formats, colors, or mounting styles than the training data","I want to improve detection on challenging conditions (night, rain, motion blur) specific to my deployment environment","I need to reduce training time and data requirements by leveraging the pre-trained weights"],"best_for":["computer vision teams with domain-specific license plate datasets (regional formats, vehicle types)","organizations deploying in environments different from the original training distribution","teams with limited labeled data (100-1000 images) who can leverage transfer learning"],"limitations":["Fine-tuning requires labeled dataset in COCO or YOLO format; annotation effort is non-trivial (10-20 minutes per image for bounding boxes)","Transfer learning assumes source and target domains are related; fine-tuning on drastically different plate types may require more data or longer training","Hyperparameter tuning (learning rate, batch size, augmentation) is manual and dataset-dependent; no automatic hyperparameter optimization","Fine-tuned models may overfit on small custom datasets (<500 images); requires careful validation and regularization (dropout, weight decay)"],"requires":["Custom labeled dataset (minimum 100-500 images for meaningful fine-tuning, ideally 1000+)","Annotations in COCO JSON or YOLO TXT format (bounding boxes with 'license-plate' class)","Python 3.7+ with PyTorch 1.9+","GPU with 4GB+ VRAM (8GB+ recommended for batch_size > 16)","Ultralytics YOLOv5 training script and dependencies"],"input_types":["custom image dataset (JPEG, PNG)","annotations (COCO JSON or YOLO TXT format)","training hyperparameters (learning rate, batch size, epochs, augmentation config)","pre-trained model weights (yolov5m.pt)"],"output_types":["fine-tuned model weights (custom_yolov5m.pt)","training logs (loss curves, mAP progression)","validation metrics (mAP, precision, recall on custom dataset)","training artifacts (tensorboard events, checkpoint weights)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-keremberke--yolov5m-license-plate__cap_8","uri":"capability://image.visual.visualization.and.annotation.of.detected.license.plates","name":"visualization and annotation of detected license plates","description":"Generates annotated images with detected license plate bounding boxes, confidence scores, and class labels overlaid on the original image. Supports customizable visualization (box colors, label fonts, confidence thresholds for display) and batch visualization of detection results across multiple images, enabling visual inspection of model performance and debugging of detection failures.","intents":["I want to visually inspect detection results to verify the model is finding plates correctly","I need to generate annotated images for reports or presentations showing model performance","I want to debug false positives or false negatives by visualizing where the model is detecting plates","I need to create a dataset of annotated examples for training or validation purposes"],"best_for":["ML engineers debugging model performance and detection failures","teams creating reports or documentation with visual examples of model output","data scientists validating annotation quality or model behavior on specific images"],"limitations":["Visualization is computationally lightweight but adds I/O overhead for large batches (writing annotated images to disk)","Overlaid text (confidence scores, labels) may be difficult to read on small plates or low-resolution images","No built-in filtering for visualization (e.g., show only detections above confidence threshold); requires manual post-processing","Batch visualization can consume significant disk space (annotated images are larger than originals)"],"requires":["Detected bounding boxes and confidence scores from model inference","Original images (JPEG, PNG, BMP, WebP)","Python 3.7+ with PIL/Pillow 8.0+ or OpenCV 4.5+ for image drawing","Optional: Matplotlib for interactive visualization"],"input_types":["image (JPEG, PNG, BMP, WebP)","bounding boxes (x_min, y_min, x_max, y_max coordinates)","confidence scores (0.0-1.0 per detection)","class labels (license-plate)","visualization config (colors, fonts, line width)"],"output_types":["annotated image (JPEG, PNG with bounding boxes and labels)","visualization report (HTML with gallery of annotated images)","batch visualization output (directory of annotated images)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+ or TensorFlow 2.4+ for inference","Python 3.7+","PIL/Pillow for image loading and preprocessing","GPU (CUDA 11.0+) recommended for batch processing; CPU inference possible but ~5-10x slower","Minimum 512MB RAM for model weights and inference buffers","PyTorch 1.9+ or TensorFlow 2.4+ or ONNX Runtime 1.10+","Ultralytics YOLOv5 library (pip install yolov5) OR direct model loading via torch.hub","Sufficient disk I/O bandwidth for image loading (SSD recommended for 1000+ images)","GPU with 2GB+ VRAM for batch inference (optional but recommended)","PIL/Pillow 8.0+ or OpenCV 4.5+ for image cropping and resizing"],"failure_modes":["Optimized for standard front-facing license plates; performance degrades on severely angled, obscured, or non-Latin character plates","YOLOv5m is a medium-weight model (~40MB); larger datasets or real-time 4K video require GPU acceleration or model quantization","No built-in handling for multiple license plates per image beyond standard NMS (non-maximum suppression) — overlapping detections may be suppressed","Trained on keremberke/license-plate-object-detection dataset; generalization to regional plate formats outside training distribution is untested","Batch processing speed is I/O-bound on CPU; GPU batch processing requires careful memory management (batch size tuning for VRAM constraints)","NMS is applied globally per image; no cross-image temporal consistency for video sequences","Confidence threshold tuning requires manual validation on representative data; no automatic threshold optimization","Output format varies by inference framework (Ultralytics, TensorFlow, ONNX); no unified serialization standard","Cropping quality depends on detection accuracy; misaligned bounding boxes produce incomplete or noisy plate regions","No automatic rotation correction for tilted plates; requires additional preprocessing if plates are at angles","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.47905291594944516,"quality":0.28,"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":46896,"model_likes":54}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=keremberke--yolov5m-license-plate","compare_url":"https://unfragile.ai/compare?artifact=keremberke--yolov5m-license-plate"}},"signature":"YUN757f33Z46z1pIKzIeZPX2Urd3npKbCEPvHej5NOSebcGuxDGeZ7ArLVfNXtpi0i1j5zhPGWeuudQ7QlDgDQ==","signedAt":"2026-06-20T15:07:25.918Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/keremberke--yolov5m-license-plate","artifact":"https://unfragile.ai/keremberke--yolov5m-license-plate","verify":"https://unfragile.ai/api/v1/verify?slug=keremberke--yolov5m-license-plate","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"}}