{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-pekingu--rtdetr_v2_r18vd","slug":"pekingu--rtdetr_v2_r18vd","name":"rtdetr_v2_r18vd","type":"model","url":"https://huggingface.co/PekingU/rtdetr_v2_r18vd","page_url":"https://unfragile.ai/pekingu--rtdetr_v2_r18vd","categories":["image-generation"],"tags":["transformers","safetensors","rt_detr_v2","object-detection","vision","en","dataset:coco","arxiv:2407.17140","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-pekingu--rtdetr_v2_r18vd__cap_0","uri":"capability://image.visual.real.time.object.detection.with.deformable.transformer.attention","name":"real-time object detection with deformable transformer attention","description":"Performs object detection on images using a deformable transformer backbone (ResNet-18 variant) combined with deformable attention mechanisms that dynamically focus on relevant spatial regions. The model uses a two-stage detection head with anchor-free predictions, enabling real-time inference (~30 FPS on standard hardware) while maintaining competitive accuracy on COCO-scale datasets. Deformable attention reduces computational overhead by sampling only task-relevant spatial locations rather than processing full feature maps.","intents":["detect and localize multiple object classes in images with low latency for real-time applications","integrate object detection into edge devices or resource-constrained environments without sacrificing accuracy","build production detection pipelines that require both speed and accuracy on diverse object categories"],"best_for":["computer vision engineers building real-time detection systems for robotics, autonomous vehicles, or surveillance","ML practitioners deploying models to edge devices or mobile platforms with strict latency budgets","teams migrating from slower two-stage detectors (Faster R-CNN) to transformer-based architectures"],"limitations":["ResNet-18 backbone limits feature extraction capacity compared to larger variants (ResNet-50, ResNet-101), reducing detection accuracy on small objects","Deformable attention adds ~15-20% computational overhead vs standard attention, impacting inference speed on very low-power devices","Requires GPU or optimized CPU inference for real-time performance; CPU-only inference may drop below 10 FPS on standard hardware","No built-in support for video-level temporal consistency — each frame processed independently without motion cues"],"requires":["PyTorch 1.9+ or TensorFlow 2.6+ (depending on framework conversion)","CUDA 11.0+ for GPU acceleration (optional but recommended for real-time performance)","Minimum 2GB GPU VRAM for batch inference; 4GB+ recommended for batch sizes >4","Input images normalized to ImageNet statistics (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])","Transformers library 4.25+ for model loading and inference"],"input_types":["image (RGB, 3-channel, variable resolution, typically 640x640 or 416x416)","batch of images (B, 3, H, W tensor format)","image file paths (JPEG, PNG, WebP)"],"output_types":["bounding boxes (x1, y1, x2, y2 or cx, cy, w, h format)","class predictions (integer class IDs with confidence scores 0-1)","structured detection results (JSON with boxes, scores, class labels)"],"categories":["image-visual","real-time-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pekingu--rtdetr_v2_r18vd__cap_1","uri":"capability://image.visual.coco.pretrained.multi.class.object.classification.and.localization","name":"coco-pretrained multi-class object classification and localization","description":"Provides pre-trained weights initialized on COCO dataset (80 object classes: person, car, dog, bicycle, etc.) enabling zero-shot or few-shot transfer to custom detection tasks. The model outputs class predictions across all 80 COCO categories with per-class confidence scores, allowing downstream filtering or class-specific post-processing. Weights are stored in safetensors format for secure, reproducible model loading without arbitrary code execution.","intents":["detect standard object categories (people, vehicles, animals, furniture) without retraining on custom datasets","perform transfer learning by fine-tuning on domain-specific objects while leveraging COCO pretraining","build multi-class detection systems that recognize diverse object types in unconstrained real-world images"],"best_for":["rapid prototyping teams needing immediate object detection without annotation effort","researchers benchmarking detection architectures against COCO-pretrained baselines","practitioners building general-purpose detection APIs that serve multiple use cases"],"limitations":["Limited to 80 COCO classes — custom object categories require fine-tuning or class remapping","Performance degrades on object categories underrepresented in COCO (e.g., rare animals, specialized equipment)","Safetensors format requires compatible loader; older PyTorch versions may need conversion to .pth format","No class-specific confidence thresholds — single global threshold applied to all 80 classes, suboptimal for imbalanced detection tasks"],"requires":["Transformers library 4.25+ with safetensors support","COCO class mapping file (typically bundled with model config)","Input images preprocessed to model's expected resolution (typically 640x640 or 416x416)"],"input_types":["RGB images with arbitrary resolution","batched image tensors (B, 3, H, W)","image URLs or local file paths"],"output_types":["class IDs (0-79 for COCO classes)","class names (string labels: 'person', 'car', 'dog', etc.)","confidence scores per class (float 0-1)","bounding box coordinates with class predictions"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pekingu--rtdetr_v2_r18vd__cap_2","uri":"capability://image.visual.batch.inference.with.dynamic.input.resolution","name":"batch inference with dynamic input resolution","description":"Processes multiple images in parallel with automatic resolution padding/resizing to handle variable input dimensions without recompilation. The model uses dynamic shape handling in the transformer backbone, allowing batch processing of images with different aspect ratios by padding to a common size and tracking valid regions. This enables efficient GPU utilization for batched inference while maintaining per-image detection accuracy.","intents":["process multiple images efficiently in a single forward pass to maximize GPU throughput","handle image streams with varying resolutions (e.g., from multiple camera sources) without separate model instances","build batch inference pipelines that balance latency and throughput for production detection systems"],"best_for":["backend engineers building high-throughput detection services processing hundreds of images/second","data scientists running batch inference on large image datasets for annotation or analysis","teams deploying detection to multi-camera systems with heterogeneous input resolutions"],"limitations":["Padding overhead increases memory usage — batch of 8 images with mixed resolutions may consume 2-3x more VRAM than 8 uniform-resolution images","Dynamic shape handling adds ~5-10% latency per batch due to padding computation and attention mask generation","Maximum batch size constrained by GPU VRAM; ResNet-18 variant supports batch 32-64 on 8GB GPU, smaller on edge devices","No built-in batching across multiple GPUs — requires external distributed inference framework (Ray, Triton) for multi-GPU scaling"],"requires":["PyTorch 1.9+ with dynamic shape support","GPU with minimum 2GB VRAM for batch size 4; 4GB+ for batch size 8+","Input images preprocessed to same aspect ratio or padded externally","Batch size tuned to hardware (typically 8-32 for optimal throughput)"],"input_types":["batch tensor (B, 3, H, W) with uniform or padded resolution","list of image tensors with variable dimensions","image file paths processed into batches"],"output_types":["batched detection results (B, N_detections, 6) where 6 = [x1, y1, x2, y2, class_id, confidence]","per-image detection lists with variable number of detections","structured JSON with per-image bounding boxes and metadata"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pekingu--rtdetr_v2_r18vd__cap_3","uri":"capability://data.processing.analysis.confidence.based.detection.filtering.and.nms.post.processing","name":"confidence-based detection filtering and nms post-processing","description":"Applies non-maximum suppression (NMS) to raw model outputs to eliminate duplicate detections of the same object, then filters results by confidence threshold. The model outputs raw class logits and box coordinates; post-processing applies softmax normalization, confidence thresholding (default 0.5), and NMS with IoU threshold (default 0.6) to produce final detections. This two-stage filtering reduces false positives and overlapping boxes typical of raw transformer outputs.","intents":["reduce duplicate detections and false positives through NMS post-processing","tune detection sensitivity by adjusting confidence thresholds for different use cases (high precision vs high recall)","produce clean, non-overlapping bounding boxes suitable for downstream applications (tracking, counting, cropping)"],"best_for":["practitioners tuning detection quality for specific applications (surveillance requires high recall; quality control requires high precision)","engineers building detection pipelines where downstream tasks (tracking, segmentation) require clean, non-overlapping boxes","teams deploying models to production where false positive rates directly impact user experience"],"limitations":["NMS is greedy and non-differentiable — cannot be included in end-to-end training, limiting joint optimization","Fixed IoU threshold (0.6) suboptimal for objects with large aspect ratio variations; may merge nearby small objects or split large objects","Confidence threshold tuning requires manual validation on held-out data; no automatic threshold selection","NMS adds ~10-20ms latency per image for large detection counts (>1000 raw detections), impacting real-time performance"],"requires":["Raw model outputs (class logits, box coordinates, objectness scores)","NMS implementation (torchvision.ops.nms or custom CUDA kernel)","Confidence threshold and IoU threshold hyperparameters tuned to application"],"input_types":["raw model outputs: class logits (B, N, 80), box coordinates (B, N, 4), objectness scores (B, N)","detection confidence scores (0-1 range)","bounding box format (x1, y1, x2, y2 or cx, cy, w, h)"],"output_types":["filtered detections with confidence > threshold","non-overlapping bounding boxes after NMS","final class predictions and confidence scores","detection count per image"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pekingu--rtdetr_v2_r18vd__cap_4","uri":"capability://automation.workflow.model.quantization.and.export.for.edge.deployment","name":"model quantization and export for edge deployment","description":"Supports conversion to quantized formats (INT8, FP16) and export to ONNX, TensorRT, or CoreML for deployment on edge devices, mobile phones, and embedded systems. The model can be quantized post-training using PyTorch quantization APIs or exported to optimized inference runtimes that reduce model size by 4-8x and latency by 2-3x compared to full-precision inference. Safetensors format enables secure, reproducible quantization without code execution risks.","intents":["deploy object detection to mobile devices (iOS, Android) or edge hardware (Jetson, Raspberry Pi) with strict size/latency constraints","reduce model size from ~50MB (FP32) to ~10-15MB (INT8) for on-device inference without cloud connectivity","optimize inference latency for real-time applications by leveraging hardware-specific quantization (e.g., TensorRT on NVIDIA GPUs)"],"best_for":["mobile app developers integrating object detection into iOS/Android applications","embedded systems engineers deploying detection to IoT devices, drones, or robotics platforms","teams building offline-capable detection systems that cannot rely on cloud inference"],"limitations":["INT8 quantization typically reduces accuracy by 1-3% mAP compared to FP32, requiring validation on target dataset","ONNX export requires manual operator mapping for deformable attention layers; some custom ops may not be supported","TensorRT optimization requires NVIDIA GPU and CUDA toolkit; not portable to other hardware","CoreML export limited to iOS; requires separate conversion pipeline for Android (ONNX → TensorFlow Lite)","Quantized models lose gradient information — cannot be fine-tuned without dequantization, limiting transfer learning"],"requires":["PyTorch 1.9+ with quantization support (torch.quantization)","ONNX opset 13+ for deformable attention export","Target platform SDK (iOS: CoreML Tools, Android: TensorFlow Lite, NVIDIA: TensorRT 8.0+)","Calibration dataset (100-500 representative images) for post-training quantization"],"input_types":["full-precision model weights (safetensors or .pth format)","calibration images for quantization statistics","model configuration (architecture, input size, class count)"],"output_types":["quantized model (INT8 or FP16 weights)","ONNX model file (.onnx)","TensorRT engine (.trt or .plan)","CoreML model (.mlmodel)","TensorFlow Lite model (.tflite)"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pekingu--rtdetr_v2_r18vd__cap_5","uri":"capability://image.visual.anchor.free.bounding.box.regression.with.iou.aware.loss","name":"anchor-free bounding box regression with iou-aware loss","description":"Predicts bounding boxes directly from image features without predefined anchor templates, using IoU-aware loss functions (e.g., GIoU, DIoU) that optimize box overlap with ground truth rather than L1/L2 distance. The model regresses box coordinates (x1, y1, x2, y2 or cx, cy, w, h) end-to-end, with loss functions that account for box geometry and overlap quality. This approach eliminates manual anchor design and improves convergence compared to anchor-based methods.","intents":["train custom object detectors without manual anchor design or tuning anchor aspect ratios","improve bounding box quality by optimizing for IoU overlap rather than coordinate distance","simplify detection pipeline by removing anchor-related hyperparameters and post-processing"],"best_for":["researchers experimenting with detection architectures without anchor engineering overhead","teams fine-tuning on custom datasets where anchor design is dataset-specific and labor-intensive","practitioners building detection systems where box quality (IoU) directly impacts downstream tasks (segmentation, tracking)"],"limitations":["Anchor-free regression may struggle with extreme aspect ratios (very wide or tall objects) without explicit feature pyramid scaling","IoU-aware loss adds ~5-10% training time overhead compared to simple L1 loss, impacting iteration speed","No built-in handling of overlapping objects of same class — may produce single merged box instead of separate detections","Requires careful initialization of box regression heads; poor initialization can lead to training instability"],"requires":["Ground truth bounding boxes in standard format (x1, y1, x2, y2 or cx, cy, w, h)","IoU-aware loss implementation (GIoU, DIoU, CIoU) — typically provided by framework","Feature pyramid or multi-scale feature extraction for handling objects at different scales"],"input_types":["image features from backbone (B, C, H, W tensor)","ground truth bounding boxes (B, N, 4)","ground truth class labels (B, N)"],"output_types":["predicted bounding boxes (B, N, 4) in same format as input","box regression confidence scores","IoU scores between predicted and ground truth boxes"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pekingu--rtdetr_v2_r18vd__cap_6","uri":"capability://image.visual.multi.scale.feature.extraction.with.feature.pyramid.network","name":"multi-scale feature extraction with feature pyramid network","description":"Extracts features at multiple scales (e.g., 1/8, 1/16, 1/32 of input resolution) using a feature pyramid network (FPN) that combines high-resolution semantic features with low-resolution spatial context. The ResNet-18 backbone produces features at multiple levels; FPN applies top-down pathways and lateral connections to create a pyramid of feature maps suitable for detecting objects at different scales. This architecture enables detection of both small objects (using high-resolution features) and large objects (using low-resolution features with larger receptive fields).","intents":["detect objects across a wide range of scales (small people in crowds, large vehicles, tiny animals) in a single forward pass","leverage multi-scale context to improve detection accuracy on objects with varying sizes","balance computational cost by processing high-resolution features only where needed (small objects) and low-resolution features for large objects"],"best_for":["practitioners building detection systems for unconstrained real-world images with objects at diverse scales","teams working with datasets containing significant scale variation (e.g., aerial imagery with objects from 10-1000 pixels)","researchers studying scale-invariant detection architectures"],"limitations":["FPN adds ~15-20% computational overhead compared to single-scale feature extraction, impacting inference latency","High-resolution feature maps (1/8 scale) consume significant GPU memory, limiting batch sizes on resource-constrained devices","Lateral connections in FPN may introduce feature misalignment at scale boundaries, causing detection artifacts","No explicit scale-aware attention — all scales processed with same deformable attention parameters, suboptimal for extreme scale variations"],"requires":["ResNet-18 backbone producing features at multiple levels (C3, C4, C5 in standard notation)","FPN implementation with top-down pathways and lateral connections","Multi-scale detection heads (one per FPN level) for predicting boxes and classes"],"input_types":["input image (3, H, W) at arbitrary resolution","backbone features at multiple scales (C3, C4, C5 with different spatial dimensions)"],"output_types":["FPN features at multiple scales (P3, P4, P5 with consistent channel dimension)","multi-scale detection outputs (boxes, classes, scores at each FPN level)","scale-specific confidence scores indicating detection quality at each scale"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pekingu--rtdetr_v2_r18vd__cap_7","uri":"capability://image.visual.transformer.based.context.aggregation.across.spatial.regions","name":"transformer-based context aggregation across spatial regions","description":"Uses transformer self-attention to aggregate contextual information across spatial regions of the image, allowing each detected object to incorporate features from distant regions. Unlike CNNs with limited receptive fields, transformer attention enables long-range spatial relationships (e.g., detecting a person holding a phone by attending to both person and phone regions). Deformable attention makes this efficient by sampling only task-relevant regions rather than all spatial locations.","intents":["improve detection accuracy by leveraging long-range spatial context (e.g., detecting objects in relation to their surroundings)","handle occlusion and partial visibility by attending to non-local features that provide semantic cues","build detection systems that understand object relationships and scene context beyond local appearance"],"best_for":["computer vision researchers studying attention mechanisms for object detection","teams building detection systems for complex scenes with significant occlusion or context dependency","practitioners working on datasets where object relationships (e.g., person-object interactions) improve detection"],"limitations":["Transformer attention adds computational overhead (~30-40% vs pure CNN) despite deformable optimization, impacting inference speed","Attention weights are difficult to interpret — understanding which regions contribute to each detection requires visualization tools","Deformable attention sampling may miss relevant regions if initial spatial focus is incorrect, leading to cascading errors","Requires sufficient training data to learn meaningful attention patterns; may overfit on small datasets compared to simpler CNN-based detectors"],"requires":["Transformer implementation (PyTorch, TensorFlow, or custom CUDA kernels)","Deformable attention modules for efficient spatial sampling","Positional encoding for spatial information (e.g., sine/cosine embeddings)","Multi-head attention configuration (typically 8-16 heads)"],"input_types":["image features from backbone (B, C, H, W)","spatial position embeddings (H, W, C_pos)","optional attention masks for padding or region-of-interest"],"output_types":["context-aggregated features (B, C, H, W) with long-range dependencies","attention weights (B, num_heads, H*W, num_samples) showing spatial relationships","detection outputs incorporating contextual information"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+ or TensorFlow 2.6+ (depending on framework conversion)","CUDA 11.0+ for GPU acceleration (optional but recommended for real-time performance)","Minimum 2GB GPU VRAM for batch inference; 4GB+ recommended for batch sizes >4","Input images normalized to ImageNet statistics (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])","Transformers library 4.25+ for model loading and inference","Transformers library 4.25+ with safetensors support","COCO class mapping file (typically bundled with model config)","Input images preprocessed to model's expected resolution (typically 640x640 or 416x416)","PyTorch 1.9+ with dynamic shape support","GPU with minimum 2GB VRAM for batch size 4; 4GB+ for batch size 8+"],"failure_modes":["ResNet-18 backbone limits feature extraction capacity compared to larger variants (ResNet-50, ResNet-101), reducing detection accuracy on small objects","Deformable attention adds ~15-20% computational overhead vs standard attention, impacting inference speed on very low-power devices","Requires GPU or optimized CPU inference for real-time performance; CPU-only inference may drop below 10 FPS on standard hardware","No built-in support for video-level temporal consistency — each frame processed independently without motion cues","Limited to 80 COCO classes — custom object categories require fine-tuning or class remapping","Performance degrades on object categories underrepresented in COCO (e.g., rare animals, specialized equipment)","Safetensors format requires compatible loader; older PyTorch versions may need conversion to .pth format","No class-specific confidence thresholds — single global threshold applied to all 80 classes, suboptimal for imbalanced detection tasks","Padding overhead increases memory usage — batch of 8 images with mixed resolutions may consume 2-3x more VRAM than 8 uniform-resolution images","Dynamic shape handling adds ~5-10% latency per batch due to padding computation and attention mask generation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.48464813220493386,"quality":0.26,"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":106918,"model_likes":5}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pekingu--rtdetr_v2_r18vd","compare_url":"https://unfragile.ai/compare?artifact=pekingu--rtdetr_v2_r18vd"}},"signature":"xGvhb0z+QSTBqmQj18gjImCINXHma552ENEkbAdU8+xv2GGjwvuxhyhUq5eXw5VT/Chl5RCVfbjXx3af5uy7CA==","signedAt":"2026-06-21T09:27:56.871Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pekingu--rtdetr_v2_r18vd","artifact":"https://unfragile.ai/pekingu--rtdetr_v2_r18vd","verify":"https://unfragile.ai/api/v1/verify?slug=pekingu--rtdetr_v2_r18vd","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"}}