{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-jameslahm--yolov10s","slug":"jameslahm--yolov10s","name":"yolov10s","type":"model","url":"https://huggingface.co/jameslahm/yolov10s","page_url":"https://unfragile.ai/jameslahm--yolov10s","categories":["image-generation"],"tags":["yolov10","safetensors","object-detection","computer-vision","pytorch_model_hub_mixin","dataset:detection-datasets/coco","arxiv:2405.14458","license:agpl-3.0","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-jameslahm--yolov10s__cap_0","uri":"capability://image.visual.real.time.multi.scale.object.detection.with.anchor.free.architecture","name":"real-time multi-scale object detection with anchor-free architecture","description":"Detects objects across images using YOLOv10's anchor-free design, which replaces traditional anchor boxes with direct bounding box regression on feature pyramids. The model processes images through a backbone (CSPDarknet-based), neck (PAN), and head that outputs class probabilities and box coordinates at multiple scales simultaneously, enabling detection of objects from small to large sizes in a single forward pass without post-hoc anchor matching.","intents":["I need to detect and localize multiple object types in images with minimal latency for real-time applications","I want to identify objects at varying scales without manually tuning anchor configurations","I need bounding box coordinates and class predictions for downstream processing or visualization"],"best_for":["computer vision engineers building real-time detection pipelines","robotics teams requiring fast object localization for control systems","autonomous vehicle perception stacks needing multi-scale detection"],"limitations":["Anchor-free approach trades some small-object detection precision vs anchor-based methods in certain domains","Inference speed varies significantly with image resolution — 640×640 baseline, scaling quadratically with larger inputs","No built-in temporal consistency across video frames — requires external tracking for video applications","COCO dataset bias means performance degrades on domain-specific objects not well-represented in training data"],"requires":["PyTorch 1.9+ with CUDA 11.0+ for GPU acceleration (CPU inference ~10x slower)","Input images must be resizable to model's expected dimensions (typically 640×640)","Minimum 2GB VRAM for batch inference; 8GB+ recommended for production throughput"],"input_types":["image (PIL Image, numpy array, torch tensor)","image batch (multiple images stacked)","video frames (sequential images)"],"output_types":["structured data (bounding boxes as [x1, y1, x2, y2] or [x_center, y_center, width, height])","class predictions (integer indices or class names)","confidence scores (float 0-1 per detection)"],"categories":["image-visual","computer-vision"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jameslahm--yolov10s__cap_1","uri":"capability://image.visual.coco.dataset.aligned.class.prediction.with.80.class.taxonomy","name":"coco dataset-aligned class prediction with 80-class taxonomy","description":"Outputs predictions mapped to the COCO dataset's 80-class taxonomy (person, car, dog, bicycle, etc.), with class indices directly corresponding to COCO category IDs. The model's final classification head produces logits for all 80 classes, which are converted to probabilities via softmax, enabling direct integration with COCO evaluation metrics and downstream applications expecting standard object categories.","intents":["I need to detect standard object categories (people, vehicles, animals) without retraining","I want predictions that map directly to COCO evaluation benchmarks for model comparison","I need class names and indices that align with existing COCO-based pipelines and datasets"],"best_for":["researchers benchmarking detection models against COCO leaderboards","teams building general-purpose detection systems for common object types","developers integrating with existing COCO-compatible annotation or evaluation tools"],"limitations":["Fixed to 80 COCO classes — cannot detect custom object types without fine-tuning","Class imbalance in COCO training data means some categories (e.g., 'toaster') have lower recall than others","No hierarchical class relationships — treats all 80 classes independently without semantic grouping"],"requires":["Knowledge of COCO class ID mapping (0-79) for interpreting raw model outputs","Optional: COCO API library for evaluation if comparing against benchmarks"],"input_types":["image"],"output_types":["class indices (0-79)","class names (string labels)","confidence scores per class"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jameslahm--yolov10s__cap_10","uri":"capability://tool.use.integration.inference.api.compatibility.via.onnx.export.and.framework.interoperability","name":"inference api compatibility via onnx export and framework interoperability","description":"Model can be exported to ONNX format for inference on non-PyTorch frameworks (TensorFlow, CoreML, TensorRT, ONNX Runtime). Export tools convert the PyTorch model to ONNX graph representation, enabling deployment on diverse inference engines. ONNX Runtime provides optimized inference across CPU, GPU, and specialized hardware (TPU, NPU) with minimal code changes.","intents":["I need to deploy YOLOv10 on non-PyTorch frameworks (TensorFlow, TensorRT, CoreML)","I want to run inference on diverse hardware (CPU, GPU, TPU, NPU) with a single model format","I need to integrate the model into production systems using ONNX Runtime"],"best_for":["teams with heterogeneous inference infrastructure (multiple frameworks/hardware)","production systems requiring framework-agnostic model deployment","developers building cross-platform applications (web, mobile, desktop)"],"limitations":["ONNX export is not officially provided — requires manual export using torch.onnx.export() or third-party tools","ONNX graph may not preserve all PyTorch operations — custom layers may fail to export","ONNX Runtime performance varies by hardware — optimization is framework-specific (TensorRT for NVIDIA, CoreML for Apple)","Quantization in ONNX requires separate quantization tools — not integrated into export pipeline","Debugging ONNX models is harder than PyTorch due to reduced introspection capabilities"],"requires":["ONNX export tool (torch.onnx or third-party exporter)","ONNX Runtime library for inference","Target framework/hardware specification for optimization"],"input_types":["PyTorch model checkpoint"],"output_types":["ONNX model file (.onnx)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jameslahm--yolov10s__cap_2","uri":"capability://image.visual.confidence.thresholded.detection.filtering.with.configurable.sensitivity","name":"confidence-thresholded detection filtering with configurable sensitivity","description":"Filters raw model predictions by confidence score threshold, suppressing low-confidence detections before output. The model outputs all candidate detections with confidence scores; users configure a threshold (typically 0.25-0.5) to retain only predictions exceeding that score, reducing false positives at the cost of potential missed detections. This filtering is applied per-image before non-maximum suppression (NMS) in inference pipelines.","intents":["I want to reduce false positive detections in my application by filtering low-confidence predictions","I need to tune detection sensitivity for my specific use case (e.g., stricter for safety-critical, looser for exploratory)","I want to balance precision vs recall by adjusting a single threshold parameter"],"best_for":["production systems where false positives have business costs (e.g., security alerts, medical imaging)","developers prototyping detection systems and iterating on sensitivity","teams deploying to resource-constrained devices needing to reduce downstream processing"],"limitations":["Threshold is global across all classes — cannot set per-class confidence requirements without custom post-processing","No adaptive thresholding based on image properties (brightness, blur, etc.)","Threshold selection is empirical; no principled method provided for choosing optimal value for new domains"],"requires":["Understanding of precision-recall tradeoff and how threshold affects both metrics","Validation dataset to empirically determine optimal threshold for your use case"],"input_types":["confidence score (float 0-1)"],"output_types":["filtered detections (subset of raw predictions above threshold)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jameslahm--yolov10s__cap_3","uri":"capability://image.visual.non.maximum.suppression.nms.with.iou.based.duplicate.removal","name":"non-maximum suppression (nms) with iou-based duplicate removal","description":"Removes duplicate or overlapping detections of the same object using intersection-over-union (IoU) calculations. After confidence filtering, NMS iteratively selects the highest-confidence detection and removes all other detections with IoU above a threshold (typically 0.45) with the selected box, preventing multiple overlapping predictions for the same object. This is applied post-inference to produce the final detection list.","intents":["I need to eliminate duplicate bounding box predictions for the same object instance","I want to tune the overlap tolerance to balance between merging nearby detections and preserving distinct objects","I need clean, non-overlapping detections for downstream tasks like tracking or counting"],"best_for":["any real-world detection pipeline where multiple overlapping predictions are undesirable","teams building object tracking systems requiring clean per-frame detections","applications counting distinct objects where duplicates would inflate counts"],"limitations":["Fixed IoU threshold across all classes — cannot preserve overlapping objects of different classes without custom logic","Greedy algorithm is not globally optimal — order-dependent and may remove valid detections if a lower-confidence detection is processed first","No temporal consistency in video — NMS is applied per-frame independently, causing detection flicker across frames"],"requires":["IoU threshold parameter (typically 0.45 for COCO, tunable per application)","Bounding box format consistency (all boxes in same coordinate system)"],"input_types":["bounding boxes (list of [x1, y1, x2, y2] or equivalent)","confidence scores (list of floats)"],"output_types":["filtered detections (subset after NMS removal)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jameslahm--yolov10s__cap_4","uri":"capability://image.visual.batch.inference.with.dynamic.image.resizing.and.padding","name":"batch inference with dynamic image resizing and padding","description":"Processes multiple images in a single forward pass by resizing and padding them to a common size (typically 640×640), stacking into a batch tensor, and running inference once. Images of different input sizes are resized (with aspect ratio preservation via letterboxing) and padded to match, enabling efficient GPU utilization. Output detections are then rescaled back to original image coordinates.","intents":["I need to process multiple images efficiently without running inference separately for each","I want to handle images of varying sizes in a single batch without manual preprocessing","I need to maximize GPU throughput by batching inference across multiple images"],"best_for":["teams processing image datasets or video streams with high throughput requirements","cloud inference services needing to amortize model loading costs across multiple requests","batch processing pipelines (e.g., daily image analysis jobs)"],"limitations":["Batch size is limited by available VRAM — typical batch size 8-32 on consumer GPUs, 64-256 on enterprise GPUs","Padding adds computational overhead for images smaller than target size (e.g., 480×480 image padded to 640×640)","Coordinate rescaling requires tracking original image dimensions; errors in rescaling produce misaligned boxes","Dynamic batch sizes require recompilation in some inference frameworks, adding latency"],"requires":["Batch size parameter (tuned to available VRAM)","Original image dimensions for rescaling output coordinates","Consistent image format across batch (e.g., all RGB, all uint8)"],"input_types":["image batch (list of images with potentially different sizes)"],"output_types":["detections in original image coordinates (rescaled from 640×640 inference space)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jameslahm--yolov10s__cap_5","uri":"capability://image.visual.multi.scale.feature.pyramid.detection.across.image.resolutions","name":"multi-scale feature pyramid detection across image resolutions","description":"Detects objects at multiple scales by processing feature maps from different depths of the backbone network through a feature pyramid network (FPN/PAN). The neck combines high-resolution shallow features (for small objects) with low-resolution deep features (for large objects), producing predictions at 3 scales (e.g., 80×80, 40×40, 20×20 feature maps corresponding to 8×, 16×, 32× downsampling). Each scale predicts objects in its receptive field range, enabling detection of objects from ~10 pixels to full-image size.","intents":["I need to detect both small objects (e.g., distant people) and large objects (e.g., vehicles) in the same image","I want the model to automatically handle scale variation without manual preprocessing","I need to understand which detections come from which scale for debugging or analysis"],"best_for":["aerial/satellite imagery analysis where scale variation is extreme","autonomous driving perception where objects range from distant vehicles to nearby pedestrians","medical imaging where pathology sizes vary widely"],"limitations":["Small-object detection remains challenging — objects <20 pixels often missed due to information loss in downsampling","Large objects may be split across multiple scale predictions, requiring NMS to merge","Computational cost increases with number of scales — 3 scales ~30% slower than single-scale inference","Feature pyramid design assumes objects fit within receptive field of a single scale; very elongated objects may be missed"],"requires":["Input image resolution ≥640×640 for effective small-object detection","Understanding of feature map scales and their corresponding object size ranges"],"input_types":["image"],"output_types":["detections with implicit scale information (can be inferred from feature map origin)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jameslahm--yolov10s__cap_6","uri":"capability://code.generation.editing.pytorch.model.serialization.and.huggingface.hub.integration","name":"pytorch model serialization and huggingface hub integration","description":"Model is distributed as a PyTorch checkpoint (.pt or .safetensors format) via HuggingFace Model Hub, enabling one-line loading via `torch.load()` or HuggingFace's `transformers` library. The model includes architecture definition, pre-trained weights, and metadata (class names, training config). SafeTensors format provides faster loading and better security than pickle-based .pt files.","intents":["I want to load a pre-trained YOLOv10 model with a single line of code","I need to integrate the model into a PyTorch training pipeline for fine-tuning","I want to download and cache the model locally for offline inference"],"best_for":["PyTorch developers building computer vision applications","researchers fine-tuning the model on custom datasets","teams using HuggingFace ecosystem tools (transformers, datasets, accelerate)"],"limitations":["PyTorch-only — no native TensorFlow, ONNX, or TensorRT exports provided in base distribution","Model loading requires downloading full checkpoint (~50-100MB depending on variant) on first use","SafeTensors format requires `safetensors` library; older PyTorch versions may not support it","No quantized variants (int8, fp16) provided — requires post-hoc quantization for mobile/edge deployment"],"requires":["PyTorch 1.9+","HuggingFace `transformers` library (optional but recommended)","Internet connection for first-time model download (unless cached locally)"],"input_types":["model checkpoint file (.pt or .safetensors)"],"output_types":["PyTorch model object (nn.Module) ready for inference or training"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jameslahm--yolov10s__cap_7","uri":"capability://code.generation.editing.fine.tuning.on.custom.datasets.with.transfer.learning","name":"fine-tuning on custom datasets with transfer learning","description":"Enables training the pre-trained YOLOv10 model on custom object detection datasets by freezing early backbone layers and training later layers + head. The model's learned feature representations from COCO transfer to new domains, reducing training time and data requirements. Fine-tuning typically requires 100-1000 annotated examples vs 10,000+ for training from scratch, using standard PyTorch optimizers (SGD, Adam) and detection loss functions (focal loss, IoU loss).","intents":["I need to detect custom objects (e.g., specific products, defects) not in COCO without training from scratch","I want to adapt the model to my domain with limited labeled data (hundreds of images)","I need to improve accuracy on my specific use case by fine-tuning on domain data"],"best_for":["teams with custom object detection tasks and limited annotation budgets","domain experts (medical, industrial) adapting general models to specialized use cases","rapid prototyping scenarios where quick model iteration is critical"],"limitations":["Requires properly annotated dataset in standard format (COCO JSON, YOLO txt, or similar) — annotation is often the bottleneck","Fine-tuning hyperparameters (learning rate, batch size, augmentation) are dataset-dependent and require tuning","Catastrophic forgetting possible if fine-tuning learning rate is too high — model may lose COCO knowledge","No built-in active learning or data selection — requires manual dataset curation for best results","Class imbalance in custom datasets can degrade performance on minority classes"],"requires":["Annotated dataset with bounding boxes (minimum 50-100 images per class recommended)","PyTorch training loop or framework (e.g., YOLOv10 official training script, Ultralytics library)","GPU with ≥8GB VRAM for reasonable fine-tuning speed","Hyperparameter tuning (learning rate typically 0.001-0.01 for fine-tuning)"],"input_types":["image dataset with bounding box annotations"],"output_types":["fine-tuned model checkpoint"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jameslahm--yolov10s__cap_8","uri":"capability://automation.workflow.inference.optimization.for.edge.deployment.quantization.ready.architecture","name":"inference optimization for edge deployment (quantization-ready architecture)","description":"Model architecture is designed to be quantization-friendly, with layer-wise precision that enables post-training quantization to int8 or fp16 without significant accuracy loss. While the base model is fp32, the architecture (skip connections, normalization layers) is compatible with standard quantization tools (PyTorch quantization, TensorRT, ONNX quantization). Quantized variants reduce model size by 4-8× and inference latency by 2-4×, enabling deployment on mobile/edge devices.","intents":["I need to deploy YOLOv10 on edge devices (mobile, embedded) with limited compute","I want to reduce model size and inference latency for real-time mobile applications","I need to quantize the model to int8 for deployment on specialized hardware (TPU, NPU)"],"best_for":["mobile app developers targeting iOS/Android with on-device inference","embedded systems engineers (Jetson, Raspberry Pi) with compute constraints","edge cloud providers (AWS Greengrass, Azure IoT) needing low-latency inference"],"limitations":["Quantization is not provided pre-trained — requires post-hoc quantization with calibration dataset","Quantization accuracy loss varies by layer — typically 1-3% mAP drop on COCO, higher on small objects","Quantized models require quantization-aware inference frameworks (TensorRT, ONNX Runtime with QDQ) — not all frameworks support it","No official quantized checkpoints provided — users must perform quantization themselves","Quantization-to-hardware mapping is framework-specific (TensorRT for NVIDIA, CoreML for Apple, etc.)"],"requires":["Quantization framework (PyTorch quantization, TensorRT, ONNX quantization, or TFLite)","Calibration dataset (100-500 representative images from target domain)","Target hardware specification (e.g., NVIDIA Jetson, Apple Neural Engine)"],"input_types":["fp32 model checkpoint"],"output_types":["quantized model (int8 or fp16)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jameslahm--yolov10s__cap_9","uri":"capability://image.visual.video.object.tracking.via.frame.by.frame.detection.with.optional.temporal.smoothing","name":"video object tracking via frame-by-frame detection with optional temporal smoothing","description":"Applies object detection to each video frame independently, producing per-frame detections that can be linked across frames using external tracking algorithms (e.g., DeepSORT, ByteTrack). While YOLOv10 itself is frame-agnostic, the consistent detection quality enables downstream tracking. Optional temporal smoothing (e.g., Kalman filtering) can reduce detection jitter across frames, improving tracking stability without modifying the model.","intents":["I need to detect and track objects across video frames","I want to count objects or measure trajectories in video","I need stable detections across frames to feed into a tracking algorithm"],"best_for":["video analytics teams building tracking pipelines","surveillance systems requiring object counting and trajectory analysis","sports analytics or autonomous vehicle perception systems"],"limitations":["YOLOv10 has no temporal awareness — detections are independent per frame, causing ID switches in tracking","Tracking quality depends entirely on downstream tracker, not the model itself","No built-in motion prediction — fast-moving objects may be missed if they move >stride pixels between frames","Video processing is sequential — cannot parallelize across frames without custom batching logic","High frame rate videos (60+ fps) may exceed real-time inference speed on consumer hardware"],"requires":["Video file or frame stream","External tracking algorithm (DeepSORT, ByteTrack, Kalman filter, etc.)","Frame rate and video resolution specifications for latency budgeting"],"input_types":["video file or frame stream"],"output_types":["per-frame detections (can be linked into tracks by external tracker)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+ with CUDA 11.0+ for GPU acceleration (CPU inference ~10x slower)","Input images must be resizable to model's expected dimensions (typically 640×640)","Minimum 2GB VRAM for batch inference; 8GB+ recommended for production throughput","Knowledge of COCO class ID mapping (0-79) for interpreting raw model outputs","Optional: COCO API library for evaluation if comparing against benchmarks","ONNX export tool (torch.onnx or third-party exporter)","ONNX Runtime library for inference","Target framework/hardware specification for optimization","Understanding of precision-recall tradeoff and how threshold affects both metrics","Validation dataset to empirically determine optimal threshold for your use case"],"failure_modes":["Anchor-free approach trades some small-object detection precision vs anchor-based methods in certain domains","Inference speed varies significantly with image resolution — 640×640 baseline, scaling quadratically with larger inputs","No built-in temporal consistency across video frames — requires external tracking for video applications","COCO dataset bias means performance degrades on domain-specific objects not well-represented in training data","Fixed to 80 COCO classes — cannot detect custom object types without fine-tuning","Class imbalance in COCO training data means some categories (e.g., 'toaster') have lower recall than others","No hierarchical class relationships — treats all 80 classes independently without semantic grouping","ONNX export is not officially provided — requires manual export using torch.onnx.export() or third-party tools","ONNX graph may not preserve all PyTorch operations — custom layers may fail to export","ONNX Runtime performance varies by hardware — optimization is framework-specific (TensorRT for NVIDIA, CoreML for Apple)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5359484211886366,"quality":0.32,"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":"2026-05-03T14:22:58.551Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":223706,"model_likes":4}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=jameslahm--yolov10s","compare_url":"https://unfragile.ai/compare?artifact=jameslahm--yolov10s"}},"signature":"or/lBXDA/dIKxwPoexnf12k/MgTiLNY6tt0WyorMxqTX2BkCM0mO47ccR+Ym/NmB0Ma13gVSS5tAKfEuwz8cAw==","signedAt":"2026-06-21T15:58:41.211Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jameslahm--yolov10s","artifact":"https://unfragile.ai/jameslahm--yolov10s","verify":"https://unfragile.ai/api/v1/verify?slug=jameslahm--yolov10s","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"}}