{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-hustvl--yolos-tiny","slug":"hustvl--yolos-tiny","name":"yolos-tiny","type":"model","url":"https://huggingface.co/hustvl/yolos-tiny","page_url":"https://unfragile.ai/hustvl--yolos-tiny","categories":["image-generation"],"tags":["transformers","pytorch","safetensors","yolos","object-detection","vision","dataset:coco","arxiv:2106.00666","license:apache-2.0","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-hustvl--yolos-tiny__cap_0","uri":"capability://image.visual.vision.transformer.based.object.detection.with.attention.weighted.region.proposals","name":"vision transformer-based object detection with attention-weighted region proposals","description":"Detects objects in images using a Vision Transformer (ViT) backbone that processes images as sequences of patches, combined with learnable object queries that attend to relevant image regions. Unlike CNN-based detectors (YOLO, Faster R-CNN), YOLOS uses pure transformer self-attention to identify and localize objects, enabling it to capture long-range spatial dependencies and learn object relationships directly from patch embeddings without hand-crafted region proposal networks.","intents":["Detect and localize multiple object classes in images with transformer-based attention mechanisms","Leverage vision transformers for object detection tasks where capturing global context is important","Run lightweight object detection on resource-constrained devices using the tiny variant"],"best_for":["Computer vision engineers building detection pipelines that prioritize architectural simplicity over CNN inductive biases","Researchers experimenting with transformer-based detection alternatives to traditional CNN detectors","Edge deployment scenarios requiring sub-100M parameter models with reasonable accuracy-latency tradeoffs"],"limitations":["Inference latency ~50-100ms per image on CPU (slower than optimized YOLO variants due to transformer overhead)","Requires fixed input resolution (typically 512x512); variable-size inputs need padding/resizing preprocessing","Smaller model capacity (tiny variant ~5.4M parameters) trades accuracy for speed compared to larger ViT backbones","No native support for real-time video processing; requires frame-by-frame inference without temporal optimization"],"requires":["PyTorch 1.9+ or TensorFlow 2.6+","transformers library 4.10.0+","PIL/Pillow for image preprocessing","CUDA 11.0+ for GPU acceleration (optional but recommended)","Minimum 2GB RAM for model loading and inference"],"input_types":["image (PIL Image, numpy array, torch tensor)","image formats: JPEG, PNG, BMP, WebP","batch inputs: multiple images as tensor with shape [batch_size, 3, height, width]"],"output_types":["structured data: bounding boxes (x_min, y_min, x_max, y_max or center_x, center_y, width, height)","class predictions: integer class IDs with confidence scores (0.0-1.0)","detection format: list of dicts with keys 'box', 'score', 'label' per image"],"categories":["image-visual","object-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hustvl--yolos-tiny__cap_1","uri":"capability://image.visual.coco.pretrained.multi.class.object.detection.with.80.object.categories","name":"coco-pretrained multi-class object detection with 80 object categories","description":"Detects 80 object classes from the COCO dataset (people, vehicles, animals, furniture, etc.) using weights pretrained on 118K training images. The model outputs bounding box coordinates and class probabilities for each detected object, with confidence thresholds typically set at 0.5 for filtering low-confidence predictions. Inference uses the pretrained checkpoint directly without requiring fine-tuning for standard COCO classes.","intents":["Detect common objects (people, cars, dogs, chairs, etc.) in images without custom training","Use pretrained weights as a foundation for transfer learning to custom object classes","Evaluate detection performance on COCO benchmark tasks"],"best_for":["Developers building object detection features for consumer applications (autonomous vehicles, surveillance, robotics)","Teams needing zero-shot detection of common objects without dataset collection or model training","Researchers comparing transformer-based detection against CNN baselines on COCO metrics"],"limitations":["Limited to 80 COCO classes; detecting objects outside this taxonomy requires fine-tuning","Performance degrades on domain-specific images (medical imaging, satellite imagery, specialized industrial equipment)","No built-in class weighting for imbalanced detection scenarios (e.g., rare object classes in COCO)","Pretrained weights assume natural RGB images; grayscale or infrared inputs require preprocessing adaptation"],"requires":["Pretrained model checkpoint (automatically downloaded from HuggingFace Hub, ~22MB for tiny variant)","transformers library with YOLOS model class","COCO class label mapping (typically 80 classes, provided in model config)"],"input_types":["image (RGB, 3-channel)","image resolution: 512x512 (standard COCO training resolution)"],"output_types":["structured data: list of detections per image","per-detection: bounding box coordinates, class ID (0-79), confidence score (float 0.0-1.0)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hustvl--yolos-tiny__cap_2","uri":"capability://data.processing.analysis.batch.inference.with.dynamic.batching.and.mixed.precision.acceleration","name":"batch inference with dynamic batching and mixed-precision acceleration","description":"Processes multiple images simultaneously using PyTorch's batching mechanism, with optional mixed-precision (FP16) inference to reduce memory footprint and accelerate computation on NVIDIA GPUs. The model accepts batched tensor inputs and returns batched outputs, enabling efficient throughput for processing image collections. Automatic mixed precision (AMP) reduces model size by ~50% in memory while maintaining accuracy through selective FP16 quantization.","intents":["Process image collections (100s-1000s of images) efficiently with reduced latency per image","Deploy detection on memory-constrained devices by enabling mixed-precision inference","Maximize GPU utilization by batching multiple images per forward pass"],"best_for":["Production systems processing image streams or batch jobs (security footage analysis, photo library scanning)","Edge devices with limited VRAM (mobile GPUs, Jetson Nano, embedded systems)","Cost-sensitive cloud deployments where reducing inference time directly reduces billing"],"limitations":["Batch size is limited by available GPU/CPU memory; typical max batch size 8-32 depending on device","Mixed-precision inference may introduce numerical instability in rare edge cases (requires validation per use case)","Dynamic batching adds complexity to deployment; requires careful handling of variable-length outputs","No built-in batching optimization for heterogeneous image sizes; all images must be resized to 512x512 before batching"],"requires":["PyTorch 1.9+ with CUDA support for GPU acceleration","NVIDIA GPU with compute capability 7.0+ for FP16 support (optional, falls back to FP32 on CPU)","torch.cuda.amp module for automatic mixed precision context manager"],"input_types":["batched tensor: shape [batch_size, 3, 512, 512]","batch_size: 1-32 depending on available memory"],"output_types":["batched detections: list of detection lists, one per image in batch","per-image: variable number of detections (0-N objects per image)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hustvl--yolos-tiny__cap_3","uri":"capability://automation.workflow.model.export.to.onnx.and.safetensors.formats.for.cross.framework.deployment","name":"model export to onnx and safetensors formats for cross-framework deployment","description":"Exports the YOLOS model to ONNX (Open Neural Network Exchange) format for inference on non-PyTorch runtimes (ONNX Runtime, TensorRT, CoreML), and to SafeTensors format for secure, efficient weight serialization. ONNX export converts the PyTorch computation graph to a framework-agnostic format with operator-level optimization, while SafeTensors provides a safer alternative to pickle-based weight storage with built-in integrity checking.","intents":["Deploy the model in production environments using ONNX Runtime for faster inference than PyTorch","Run detection on mobile/edge devices (iOS, Android, embedded Linux) via ONNX or CoreML conversion","Safely load and verify model weights without pickle deserialization vulnerabilities"],"best_for":["DevOps/MLOps engineers deploying models across heterogeneous inference stacks (cloud, edge, mobile)","Security-conscious teams avoiding pickle-based weight loading due to code execution risks","Mobile app developers integrating object detection without PyTorch runtime overhead"],"limitations":["ONNX export requires manual operator mapping for custom layers; standard YOLOS exports cleanly but custom modifications may fail","ONNX Runtime inference adds ~5-10% latency overhead vs native PyTorch on GPU due to graph interpretation","SafeTensors format is read-only after export; requires re-export to update weights","Mobile ONNX inference (CoreML, TFLite) requires additional quantization/optimization steps not included in base export"],"requires":["transformers library with ONNX export utilities","onnx and onnxruntime packages for validation and inference","safetensors library for SafeTensors format support","torch.onnx module (included in PyTorch 1.9+)"],"input_types":["PyTorch model checkpoint (.pt, .pth, or HuggingFace Hub identifier)"],"output_types":["ONNX model file (.onnx, ~22MB for tiny variant)","SafeTensors weight file (.safetensors, ~22MB)","ONNX graph: serialized computation graph with operator definitions"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hustvl--yolos-tiny__cap_4","uri":"capability://data.processing.analysis.fine.tuning.on.custom.object.detection.datasets.with.transfer.learning","name":"fine-tuning on custom object detection datasets with transfer learning","description":"Enables transfer learning by unfreezing model layers and training on custom datasets with COCO-style annotations (bounding boxes + class labels). The pretrained COCO weights serve as initialization, reducing training time and data requirements compared to training from scratch. Fine-tuning uses standard PyTorch training loops with loss functions (Hungarian matching loss for DETR-style detectors) and gradient-based optimization.","intents":["Adapt the model to detect custom object classes (e.g., specific product SKUs, industrial defects, medical conditions)","Improve detection accuracy on domain-specific images (e.g., aerial imagery, medical scans) by fine-tuning on labeled data","Build detection models with limited labeled data by leveraging COCO pretraining"],"best_for":["Computer vision teams with 100-10K labeled images of custom objects","Researchers experimenting with transfer learning from COCO to specialized domains","Startups building domain-specific detection products without large annotation budgets"],"limitations":["Requires COCO-format annotations (bounding boxes); no built-in support for polygon/segmentation masks","Fine-tuning on very small datasets (<100 images) risks overfitting; requires careful regularization and validation","Training time: ~2-8 hours on single GPU for 10K images (vs 24+ hours for training from scratch)","No built-in active learning or data augmentation strategies; requires manual implementation or external libraries","Hyperparameter tuning (learning rate, batch size, warmup steps) is critical and dataset-dependent"],"requires":["Custom dataset with COCO-format annotations (JSON with image metadata, bounding boxes, category IDs)","PyTorch 1.9+, transformers 4.10.0+, datasets library for data loading","GPU with 8GB+ VRAM for training (can use gradient accumulation for smaller GPUs)","Training framework: PyTorch Lightning, Hugging Face Trainer, or custom training loop"],"input_types":["COCO-format JSON annotation file with structure: {images: [...], annotations: [...], categories: [...]}"],"output_types":["fine-tuned model checkpoint (.pt or SafeTensors format)","training logs: loss curves, validation metrics (mAP, precision, recall)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hustvl--yolos-tiny__cap_5","uri":"capability://data.processing.analysis.confidence.based.detection.filtering.and.non.maximum.suppression.nms","name":"confidence-based detection filtering and non-maximum suppression (nms)","description":"Filters detected objects by confidence threshold (default 0.5) to remove low-confidence predictions, then applies non-maximum suppression (NMS) to eliminate duplicate detections of the same object. NMS iteratively removes lower-confidence boxes that overlap significantly (IoU > threshold, typically 0.5) with higher-confidence boxes, reducing false positives from multiple overlapping predictions.","intents":["Remove low-confidence predictions to reduce false positives in detection output","Eliminate duplicate detections when the model predicts multiple overlapping boxes for the same object","Tune detection sensitivity by adjusting confidence and NMS thresholds per application"],"best_for":["Production detection pipelines requiring clean, non-redundant outputs","Applications with strict false-positive budgets (security, medical imaging)","Developers tuning detection sensitivity for specific use cases"],"limitations":["NMS is a greedy algorithm; may fail to separate closely-spaced objects (e.g., crowded scenes with overlapping people)","Fixed IoU threshold (typically 0.5) is suboptimal for objects of varying sizes; soft-NMS or class-specific thresholds require custom implementation","Confidence threshold is global; no per-class threshold tuning without post-processing","NMS adds ~5-10ms latency per image; not negligible for real-time applications"],"requires":["Detection outputs with bounding boxes and confidence scores","torchvision.ops.nms or custom NMS implementation","Configurable confidence and IoU thresholds"],"input_types":["detections: list of dicts with 'box' (4 coordinates), 'score' (float 0-1), 'label' (int)"],"output_types":["filtered detections: subset of input detections after confidence and NMS filtering","per-detection: same format as input (box, score, label)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-hustvl--yolos-tiny__cap_6","uri":"capability://automation.workflow.inference.on.cpu.with.quantization.support.for.resource.constrained.environments","name":"inference on cpu with quantization support for resource-constrained environments","description":"Runs object detection on CPU without GPU acceleration, with optional 8-bit integer quantization (INT8) to reduce model size by ~75% and accelerate inference on CPU-only devices. Quantization maps floating-point weights to 8-bit integers, reducing memory bandwidth and enabling faster computation on CPUs without specialized hardware. Inference uses standard PyTorch CPU kernels or quantized inference engines (ONNX Runtime with QNN backend).","intents":["Deploy detection on CPU-only devices (older servers, embedded systems, Raspberry Pi)","Reduce model size from 22MB to ~6MB for deployment on bandwidth-constrained networks","Run inference without GPU dependency for cost-sensitive or offline scenarios"],"best_for":["Edge device developers (IoT, embedded systems, Raspberry Pi, Jetson Nano with CPU fallback)","Cost-conscious deployments avoiding GPU infrastructure","Offline/on-device inference scenarios without cloud connectivity"],"limitations":["CPU inference is 10-50x slower than GPU (50-100ms per image on modern CPU vs 5-10ms on GPU)","INT8 quantization reduces accuracy by 1-3% on COCO metrics; requires validation per use case","Quantization requires calibration on representative data; no pre-quantized checkpoints provided","Limited parallelization on multi-core CPUs; batch processing gains are minimal vs single-image inference","No built-in support for CPU-specific optimizations (SIMD, AVX-512); requires ONNX Runtime or TVM for acceleration"],"requires":["PyTorch CPU build (default installation)","Optional: ONNX Runtime with CPU providers for faster inference","Optional: quantization libraries (torch.quantization, ONNX Runtime QNN backend)","Calibration dataset for INT8 quantization (100-1000 representative images)"],"input_types":["image (same as GPU inference)"],"output_types":["detections (same format as GPU inference, with 1-3% accuracy variance from quantization)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+ or TensorFlow 2.6+","transformers library 4.10.0+","PIL/Pillow for image preprocessing","CUDA 11.0+ for GPU acceleration (optional but recommended)","Minimum 2GB RAM for model loading and inference","Pretrained model checkpoint (automatically downloaded from HuggingFace Hub, ~22MB for tiny variant)","transformers library with YOLOS model class","COCO class label mapping (typically 80 classes, provided in model config)","PyTorch 1.9+ with CUDA support for GPU acceleration","NVIDIA GPU with compute capability 7.0+ for FP16 support (optional, falls back to FP32 on CPU)"],"failure_modes":["Inference latency ~50-100ms per image on CPU (slower than optimized YOLO variants due to transformer overhead)","Requires fixed input resolution (typically 512x512); variable-size inputs need padding/resizing preprocessing","Smaller model capacity (tiny variant ~5.4M parameters) trades accuracy for speed compared to larger ViT backbones","No native support for real-time video processing; requires frame-by-frame inference without temporal optimization","Limited to 80 COCO classes; detecting objects outside this taxonomy requires fine-tuning","Performance degrades on domain-specific images (medical imaging, satellite imagery, specialized industrial equipment)","No built-in class weighting for imbalanced detection scenarios (e.g., rare object classes in COCO)","Pretrained weights assume natural RGB images; grayscale or infrared inputs require preprocessing adaptation","Batch size is limited by available GPU/CPU memory; typical max batch size 8-32 depending on device","Mixed-precision inference may introduce numerical instability in rare edge cases (requires validation per use case)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.55497745326808,"quality":0.24,"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":83525,"model_likes":281}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=hustvl--yolos-tiny","compare_url":"https://unfragile.ai/compare?artifact=hustvl--yolos-tiny"}},"signature":"i0quLfPZth174xZuOtn5ZgI9y443Z8aahgOaU/uKGUdLfr2Wo/TaOxRkwyLrFD0uIt+l7IL/UcMAXW+9zcXUAw==","signedAt":"2026-06-20T05:32:47.017Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hustvl--yolos-tiny","artifact":"https://unfragile.ai/hustvl--yolos-tiny","verify":"https://unfragile.ai/api/v1/verify?slug=hustvl--yolos-tiny","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"}}