{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-anzhc--anzhcs_yolos","slug":"anzhc--anzhcs_yolos","name":"Anzhcs_YOLOs","type":"model","url":"https://huggingface.co/Anzhc/Anzhcs_YOLOs","page_url":"https://unfragile.ai/anzhc--anzhcs_yolos","categories":["image-generation"],"tags":["ultralytics","pytorch","YOLOv8","art","Ultralytics","object-detection","base_model:Ultralytics/YOLO11","base_model:finetune:Ultralytics/YOLO11","license:agpl-3.0","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-anzhc--anzhcs_yolos__cap_0","uri":"capability://image.visual.real.time.multi.class.object.detection.with.bounding.box.localization","name":"real-time multi-class object detection with bounding box localization","description":"Detects and localizes multiple object classes in images using YOLOv8/YOLO11 architecture with convolutional neural networks optimized for speed-accuracy tradeoff. The model processes images end-to-end through a single-stage detector that predicts class probabilities and bounding box coordinates simultaneously, enabling real-time inference on CPU and GPU hardware. Fine-tuned on Ultralytics base weights with custom art-domain training data to specialize detection for specific object categories.","intents":["I need to detect specific objects in images and get their precise locations as bounding boxes","I want to run object detection inference locally without cloud API calls for privacy or latency reasons","I need to identify and count multiple instances of objects in artwork or images","I want to integrate object detection into a computer vision pipeline with minimal setup overhead"],"best_for":["Computer vision engineers building production detection pipelines","Developers prototyping object detection features without cloud dependencies","Teams working with art/creative content requiring domain-specific detection","Edge device deployments requiring sub-100ms inference latency"],"limitations":["Model performance degrades on objects significantly smaller than training resolution (typically <32px)","Inference speed varies 5-50x depending on hardware (CPU vs GPU); CPU inference may be 10-20x slower than GPU","No built-in multi-frame temporal consistency — each frame processed independently without motion tracking","Fine-tuning data and training procedure unknown; generalization to out-of-domain objects unpredictable","Requires image preprocessing (resizing, normalization) before inference; no automatic input adaptation"],"requires":["PyTorch 1.9+ (YOLO11 requires 2.0+ for optimal performance)","Ultralytics library (pip install ultralytics)","Python 3.8+","GPU with CUDA 11.8+ recommended for real-time inference; CPU inference possible but slow","Input images in standard formats (JPEG, PNG, BMP, WebP)"],"input_types":["image (JPEG, PNG, BMP, WebP, TIFF)","image batch (multiple images for batch inference)","video frames (as image sequences)"],"output_types":["structured data (bounding boxes as [x_min, y_min, x_max, y_max] or [x_center, y_center, width, height])","class predictions (integer class IDs with confidence scores 0.0-1.0)","annotated images (visualization with boxes and labels overlaid)"],"categories":["image-visual","object-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-anzhc--anzhcs_yolos__cap_1","uri":"capability://image.visual.batch.inference.with.configurable.confidence.thresholding.and.nms.filtering","name":"batch inference with configurable confidence thresholding and nms filtering","description":"Processes multiple images in parallel batches through the YOLO11 model with post-processing that filters detections by confidence score and applies Non-Maximum Suppression (NMS) to remove duplicate overlapping boxes. The implementation supports configurable IoU (Intersection over Union) thresholds for NMS and confidence cutoffs, enabling users to trade recall for precision based on downstream task requirements. Ultralytics framework handles batch dimension optimization automatically across CPU/GPU.","intents":["I need to process 100+ images efficiently without calling the model 100+ times","I want to filter out low-confidence detections to reduce false positives in my pipeline","I need to remove duplicate overlapping bounding boxes from the same object","I want to tune detection sensitivity (confidence threshold) for different use cases without retraining"],"best_for":["Data processing teams running batch inference on image datasets","Production systems requiring configurable detection sensitivity per deployment","Developers optimizing inference throughput on limited hardware","Quality assurance workflows filtering detections by confidence scores"],"limitations":["Batch size limited by GPU VRAM; typical max 32-128 images per batch on consumer GPUs","NMS is greedy algorithm — does not guarantee globally optimal box selection for heavily overlapping objects","Confidence threshold and NMS IoU are global settings; cannot vary per-class without post-processing","No adaptive batching — fixed batch size regardless of image resolution or complexity","Post-processing (NMS, filtering) adds 5-15ms per batch regardless of batch size"],"requires":["PyTorch 1.9+","Ultralytics library with batch inference support","Python 3.8+","GPU memory proportional to batch size (roughly 1-2GB per 32-image batch for YOLO11)"],"input_types":["image batch (list of image paths or numpy arrays)","image directory (automatic batch loading from folder)"],"output_types":["structured data (list of detection results per image with boxes, classes, confidence scores)","filtered detections (post-NMS, post-confidence-threshold)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-anzhc--anzhcs_yolos__cap_2","uri":"capability://image.visual.model.export.to.multiple.inference.frameworks.and.hardware.targets","name":"model export to multiple inference frameworks and hardware targets","description":"Exports the fine-tuned YOLO11 model to optimized formats including ONNX, TensorRT, CoreML, and OpenVINO, enabling deployment across diverse hardware (edge devices, mobile, cloud servers, browsers). The export pipeline automatically handles quantization, graph optimization, and format-specific conversions while preserving model accuracy. Ultralytics framework manages the export process end-to-end without manual graph manipulation.","intents":["I need to deploy this model on edge devices (Raspberry Pi, Jetson) without PyTorch runtime","I want to run inference in the browser or on iOS/Android without cloud calls","I need to optimize model size and latency for production deployment on cloud servers","I want to use this model with different inference engines (TensorRT for NVIDIA, CoreML for Apple)"],"best_for":["Edge ML engineers deploying to resource-constrained devices","Mobile app developers targeting iOS/Android platforms","Production teams optimizing inference latency and model size","Cross-platform deployment teams supporting multiple hardware targets"],"limitations":["Export to TensorRT requires NVIDIA GPU with CUDA 11.8+; not portable to non-NVIDIA hardware","CoreML export limited to macOS/iOS; no Android native support without additional conversion","ONNX export may lose some Ultralytics-specific optimizations; requires ONNX Runtime for inference","Quantization (int8, fp16) may reduce accuracy by 1-5% depending on calibration data","Export process takes 2-10 minutes depending on target format and model size"],"requires":["PyTorch 1.9+ with original model weights","Ultralytics library with export modules","Target framework installed (ONNX Runtime, TensorRT, CoreML tools, OpenVINO toolkit)","Python 3.8+","For TensorRT: NVIDIA GPU with CUDA 11.8+","For CoreML: macOS 10.15+ or Xcode"],"input_types":["PyTorch model weights (.pt file)","Model configuration (YAML or Python object)"],"output_types":["ONNX model (.onnx file)","TensorRT engine (.engine file)","CoreML model (.mlmodel file)","OpenVINO IR (.xml + .bin files)","SavedModel format (TensorFlow)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-anzhc--anzhcs_yolos__cap_3","uri":"capability://image.visual.fine.tuning.on.custom.datasets.with.transfer.learning","name":"fine-tuning on custom datasets with transfer learning","description":"Enables retraining the YOLO11 base model on custom annotated datasets using transfer learning, where pre-trained weights from Ultralytics base model are used as initialization and only updated for new object classes or domain-specific patterns. The training pipeline handles data augmentation (mosaic, mixup, rotation, scaling), automatic anchor generation, and multi-scale training. Loss functions (box regression, classification, objectness) are optimized jointly across all scales.","intents":["I want to adapt this model to detect custom objects in my domain without training from scratch","I need to improve detection accuracy on my specific dataset by fine-tuning the pre-trained weights","I want to add new object classes to the model while retaining knowledge of original classes","I need to train on limited labeled data (100-1000 images) without overfitting"],"best_for":["ML engineers with domain-specific object detection tasks and labeled datasets","Teams with 100-10,000 annotated images wanting to improve accuracy over base model","Researchers experimenting with transfer learning and data augmentation strategies","Companies building custom detection models for proprietary objects or scenes"],"limitations":["Requires properly annotated dataset in YOLO format (bounding box coordinates + class labels); annotation effort is significant","Training time 1-48 hours depending on dataset size and hardware; GPU strongly recommended","Overfitting risk with small datasets (<500 images); requires careful hyperparameter tuning and augmentation","Fine-tuning on significantly different domain (e.g., medical imaging) may require more data than transfer learning typically assumes","No automatic hyperparameter optimization; users must manually tune learning rate, batch size, augmentation intensity"],"requires":["PyTorch 1.9+","Ultralytics library with training modules","Python 3.8+","GPU with 6GB+ VRAM (RTX 3060 or better recommended)","Annotated dataset in YOLO format (.txt files with normalized bounding box coordinates)","Dataset split into train/val/test directories"],"input_types":["image dataset (JPEG, PNG in directory structure)","annotation files (YOLO format: class_id x_center y_center width height normalized to 0-1)","dataset.yaml configuration file (paths, class names, number of classes)"],"output_types":["fine-tuned model weights (.pt file)","training metrics (loss curves, mAP scores, confusion matrices)","validation results (per-class precision/recall, detection visualizations)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-anzhc--anzhcs_yolos__cap_4","uri":"capability://image.visual.multi.scale.inference.with.dynamic.input.resolution","name":"multi-scale inference with dynamic input resolution","description":"Supports inference on images of arbitrary resolution by automatically resizing to model input size (typically 640x640) while preserving aspect ratio through letterboxing or padding. The model processes variable-resolution inputs without retraining; inference pipeline handles pre-processing (normalization, tensor conversion) and post-processing (coordinate scaling back to original image space). Enables detection on high-resolution images by tiling or multi-scale inference strategies.","intents":["I need to run detection on images of different sizes without manually resizing each one","I want to detect small objects in high-resolution images by running inference at multiple scales","I need to preserve original image coordinates in detection output for downstream processing","I want to process video frames of varying resolution without changing model configuration"],"best_for":["Computer vision pipelines handling diverse image sources (webcams, photos, screenshots)","Applications requiring detection on high-resolution images (4K, 8K) with small object preservation","Video processing systems with variable frame resolutions","Production systems needing robust handling of arbitrary input dimensions"],"limitations":["Inference latency increases with image resolution (quadratic relationship); 4K images ~4x slower than 640p","Letterboxing adds padding that may affect detection near image borders (false positives on padding artifacts)","Multi-scale inference (running model at multiple resolutions) requires multiple forward passes, multiplying compute cost","No automatic optimal resolution selection; users must manually choose inference resolution vs accuracy tradeoff","Coordinate transformation back to original space introduces rounding errors for very small objects"],"requires":["PyTorch 1.9+","Ultralytics library","Python 3.8+","Input images in standard formats (JPEG, PNG, BMP, WebP, TIFF)"],"input_types":["image (arbitrary resolution, any aspect ratio)","image batch (mixed resolutions, automatically padded to common size)"],"output_types":["detections in original image coordinate space (bounding boxes scaled back to input dimensions)","confidence scores and class predictions"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+ (YOLO11 requires 2.0+ for optimal performance)","Ultralytics library (pip install ultralytics)","Python 3.8+","GPU with CUDA 11.8+ recommended for real-time inference; CPU inference possible but slow","Input images in standard formats (JPEG, PNG, BMP, WebP)","PyTorch 1.9+","Ultralytics library with batch inference support","GPU memory proportional to batch size (roughly 1-2GB per 32-image batch for YOLO11)","PyTorch 1.9+ with original model weights","Ultralytics library with export modules"],"failure_modes":["Model performance degrades on objects significantly smaller than training resolution (typically <32px)","Inference speed varies 5-50x depending on hardware (CPU vs GPU); CPU inference may be 10-20x slower than GPU","No built-in multi-frame temporal consistency — each frame processed independently without motion tracking","Fine-tuning data and training procedure unknown; generalization to out-of-domain objects unpredictable","Requires image preprocessing (resizing, normalization) before inference; no automatic input adaptation","Batch size limited by GPU VRAM; typical max 32-128 images per batch on consumer GPUs","NMS is greedy algorithm — does not guarantee globally optimal box selection for heavily overlapping objects","Confidence threshold and NMS IoU are global settings; cannot vary per-class without post-processing","No adaptive batching — fixed batch size regardless of image resolution or complexity","Post-processing (NMS, filtering) adds 5-15ms per batch regardless of batch size","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5465577291950684,"quality":0.2,"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":"2026-05-24T12:16:22.764Z","last_scraped_at":"2026-05-03T14:22:58.551Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":86897,"model_likes":168}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=anzhc--anzhcs_yolos","compare_url":"https://unfragile.ai/compare?artifact=anzhc--anzhcs_yolos"}},"signature":"L6hUbYQLlHuc5sRwZuuu25XFjOO9X34R1dKRntfV29/kXLl1URp0EEwLP+3PJ+v8rLPQx7ejjE6SpHvslSAWDw==","signedAt":"2026-06-22T19:52:03.211Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/anzhc--anzhcs_yolos","artifact":"https://unfragile.ai/anzhc--anzhcs_yolos","verify":"https://unfragile.ai/api/v1/verify?slug=anzhc--anzhcs_yolos","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"}}