{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-facebook--detr-resnet-50","slug":"facebook--detr-resnet-50","name":"detr-resnet-50","type":"model","url":"https://huggingface.co/facebook/detr-resnet-50","page_url":"https://unfragile.ai/facebook--detr-resnet-50","categories":["image-generation"],"tags":["transformers","pytorch","safetensors","detr","object-detection","vision","dataset:coco","arxiv:2005.12872","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-facebook--detr-resnet-50__cap_0","uri":"capability://image.visual.end.to.end.transformer.based.object.detection.with.resnet.50.backbone","name":"end-to-end transformer-based object detection with resnet-50 backbone","description":"Performs object detection by treating detection as a direct set prediction problem using a transformer encoder-decoder architecture with a ResNet-50 CNN backbone for feature extraction. The model uses bipartite matching (Hungarian algorithm) to assign predictions to ground-truth objects, eliminating the need for hand-designed components like NMS or anchor boxes. It outputs bounding boxes and class labels directly from transformer decoder outputs without post-processing.","intents":["detect and localize multiple objects in images with class labels and confidence scores","integrate object detection into computer vision pipelines without anchor engineering","benchmark detection performance on COCO dataset with transformer-based architecture","deploy production object detection with minimal post-processing overhead"],"best_for":["computer vision engineers building detection pipelines who want transformer-based alternatives to Faster R-CNN/YOLOv3","researchers prototyping detection models with minimal architectural complexity","teams deploying detection on edge/cloud with standardized transformer inference"],"limitations":["slower inference than YOLO variants (~100ms per image on GPU) due to transformer decoder sequential processing","requires fixed input resolution or padding; aspect ratio changes degrade performance","bipartite matching adds computational overhead during training; inference speed not optimized for real-time video (< 30 FPS on consumer GPUs)","struggles with small objects and crowded scenes compared to anchor-based methods due to set prediction formulation","no native support for panoptic segmentation or instance segmentation masks"],"requires":["PyTorch 1.9+","torchvision with DETR model definitions","transformers library 4.5.0+","CUDA 11.0+ for GPU inference (CPU inference supported but slow)","minimum 4GB VRAM for batch inference"],"input_types":["PIL Image","numpy array (H, W, 3) with uint8 or float32 values","torch.Tensor (B, 3, H, W) normalized to ImageNet stats","image file paths (JPEG, PNG)"],"output_types":["structured predictions: logits (B, num_queries, num_classes), boxes (B, num_queries, 4)","post-processed detections: list of dicts with 'scores', 'labels', 'boxes' tensors","JSON with bounding box coordinates (x, y, width, height) and class names"],"categories":["image-visual","deep-learning-computer-vision"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--detr-resnet-50__cap_1","uri":"capability://image.visual.resnet.50.cnn.feature.extraction.with.imagenet.pretraining","name":"resnet-50 cnn feature extraction with imagenet pretraining","description":"Extracts multi-scale visual features from input images using a pretrained ResNet-50 backbone (trained on ImageNet-1k). The backbone outputs a feature map at 1/32 resolution of the input, which is then flattened and projected into the transformer embedding space. ResNet-50 uses residual connections and batch normalization to enable training of 50-layer networks, providing a proven feature extractor that balances accuracy and computational efficiency.","intents":["leverage ImageNet-pretrained weights to reduce training time and improve detection accuracy","extract spatial features at multiple scales for transformer encoder input","use a well-established CNN backbone with known performance characteristics"],"best_for":["practitioners who want proven feature extraction without training from scratch","teams with limited compute budgets who benefit from transfer learning"],"limitations":["fixed to ResNet-50 architecture; no option for lighter backbones (ResNet-18) or heavier ones (ResNet-101) in this specific model checkpoint","ImageNet pretraining introduces dataset bias toward natural images; performance degrades on medical, satellite, or synthetic imagery","1/32 spatial resolution may lose fine details for small objects"],"requires":["torchvision 0.10.0+","ImageNet normalization stats (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])"],"input_types":["torch.Tensor (B, 3, H, W) with ImageNet normalization applied"],"output_types":["torch.Tensor (B, 2048, H/32, W/32) feature maps"],"categories":["image-visual","deep-learning-computer-vision"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--detr-resnet-50__cap_2","uri":"capability://image.visual.transformer.encoder.decoder.with.learned.object.queries.for.set.prediction","name":"transformer encoder-decoder with learned object queries for set prediction","description":"Implements a transformer encoder-decoder stack where the encoder processes CNN features and the decoder uses N learned object query embeddings (typically 100) to predict a fixed-size set of detections. Each query attends to the entire feature map via multi-head self-attention, enabling the model to reason about object relationships and spatial context. The decoder outputs logits for class prediction and bounding box regression for each query, treating detection as a set prediction problem rather than spatial grid-based prediction.","intents":["predict a variable number of objects (up to N queries) without anchor engineering","enable transformer attention mechanisms to model object relationships and context","output detection predictions as an unordered set with bipartite matching to ground truth"],"best_for":["researchers exploring transformer-based detection architectures","teams building detection systems where interpretability of attention patterns is valuable"],"limitations":["fixed number of queries (100) means maximum 100 detections per image; sparse scenes waste computation, crowded scenes may miss objects","transformer decoder is autoregressive during training but parallel during inference, creating train-test mismatch","attention computation is O(N²) in sequence length, making very high-resolution features expensive","learned queries have no explicit spatial grounding; model must learn spatial reasoning from scratch"],"requires":["transformers library 4.5.0+","PyTorch 1.9+ with CUDA support for efficient attention computation"],"input_types":["torch.Tensor (B, C, H, W) feature maps from CNN backbone"],"output_types":["class logits (B, num_queries, num_classes)","bounding box predictions (B, num_queries, 4) in normalized coordinates"],"categories":["image-visual","deep-learning-computer-vision"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--detr-resnet-50__cap_3","uri":"capability://image.visual.bipartite.matching.loss.with.hungarian.algorithm.for.training","name":"bipartite matching loss with hungarian algorithm for training","description":"Trains the model using bipartite matching between predicted detections and ground-truth objects via the Hungarian algorithm, which finds the optimal one-to-one assignment minimizing total matching cost. The cost combines classification loss (cross-entropy) and bounding box regression loss (L1 + GIoU). This eliminates the need for NMS or anchor assignment heuristics, treating detection as a pure set matching problem where the model learns to predict exactly one detection per object.","intents":["train object detection without hand-tuned anchor assignment rules","optimize detection predictions as an optimal assignment problem","enable end-to-end differentiable training without NMS"],"best_for":["researchers implementing DETR-style detection from scratch","teams fine-tuning DETR on custom datasets with varying object distributions"],"limitations":["Hungarian algorithm adds ~50-100ms per training step on CPU; requires scipy.optimize.linear_sum_assignment","bipartite matching assumes one-to-one object assignment; fails gracefully on overlapping objects but may miss detections","training is slower than anchor-based methods due to matching overhead and lack of hard negative mining","requires careful loss weighting between classification and regression terms; sensitive to hyperparameter tuning"],"requires":["scipy 1.5.0+ for linear_sum_assignment","PyTorch 1.9+ for gradient computation through matching"],"input_types":["predicted logits (B, num_queries, num_classes)","predicted boxes (B, num_queries, 4)","ground-truth labels (B, num_objects)","ground-truth boxes (B, num_objects, 4)"],"output_types":["scalar loss value for backpropagation","matching indices for analysis"],"categories":["image-visual","deep-learning-computer-vision"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--detr-resnet-50__cap_4","uri":"capability://image.visual.coco.dataset.evaluation.with.standard.metrics.ap.ap50.ap75","name":"coco dataset evaluation with standard metrics (ap, ap50, ap75)","description":"Evaluates detection performance using COCO Average Precision (AP) metrics, which measure detection quality across IoU thresholds (AP@0.5:0.95 is the primary metric). The model outputs predictions in COCO format (image_id, category_id, bbox, score) which are compared against ground-truth annotations using the official COCO evaluation script. Metrics include AP (average across IoU thresholds), AP50 (IoU=0.5), AP75 (IoU=0.75), and separate metrics for small/medium/large objects.","intents":["benchmark detection performance against published COCO leaderboards","evaluate model quality using standard metrics for comparison with other detectors","identify performance gaps on small vs large objects"],"best_for":["researchers publishing detection results and comparing against baselines","teams evaluating model quality on standard benchmarks"],"limitations":["COCO metrics are compute-intensive; evaluation on full validation set (5k images) takes 5-10 minutes","AP metrics are sensitive to confidence thresholds and NMS parameters; small changes can shift scores by 1-2 AP","COCO dataset bias toward natural images; metrics may not reflect performance on domain-specific data (medical, satellite)","no built-in support for custom metrics or domain-specific evaluation"],"requires":["pycocotools 2.0.2+","COCO dataset annotations in official JSON format","predictions in COCO format with image_id, category_id, bbox, score"],"input_types":["COCO-format predictions JSON","COCO-format ground-truth annotations JSON"],"output_types":["AP (average precision across IoU thresholds)","AP50, AP75 (at specific IoU thresholds)","APsmall, APmedium, APlarge (by object size)","per-category AP scores"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--detr-resnet-50__cap_5","uri":"capability://image.visual.inference.with.post.processing.and.confidence.thresholding","name":"inference with post-processing and confidence thresholding","description":"Performs inference by running the model forward pass and post-processing raw predictions: filtering detections by confidence score threshold, converting normalized box coordinates to pixel coordinates, and optionally applying soft-NMS for overlapping detections. The model outputs logits and box deltas which are converted to class probabilities via softmax and box coordinates via inverse normalization. Post-processing is minimal compared to anchor-based methods but still includes confidence filtering and coordinate transformation.","intents":["run inference on new images and extract detection results","filter low-confidence predictions to reduce false positives","convert model outputs to standard bounding box format for downstream processing"],"best_for":["practitioners deploying DETR for inference on new data","teams integrating detection into production pipelines"],"limitations":["inference speed ~100ms per image on GPU (slower than YOLO/EfficientDet), not suitable for real-time video","confidence threshold is a hyperparameter requiring tuning for each application; no automatic threshold selection","no built-in batching optimization; batch inference is slower per-image than single-image inference due to padding overhead","post-processing is minimal (no NMS by default); overlapping detections may not be suppressed"],"requires":["PyTorch 1.9+","transformers library 4.5.0+","input images normalized to ImageNet statistics"],"input_types":["PIL Image","numpy array (H, W, 3)","torch.Tensor (B, 3, H, W)"],"output_types":["detections dict with 'scores', 'labels', 'boxes' tensors","JSON with bounding boxes in (x, y, width, height) format","COCO-format predictions"],"categories":["image-visual","deep-learning-computer-vision"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--detr-resnet-50__cap_6","uri":"capability://image.visual.fine.tuning.on.custom.datasets.with.transfer.learning","name":"fine-tuning on custom datasets with transfer learning","description":"Enables fine-tuning the pretrained model on custom object detection datasets by unfreezing the backbone and decoder weights and training with the bipartite matching loss. The model leverages ImageNet-pretrained ResNet-50 features as initialization, reducing training time and data requirements compared to training from scratch. Fine-tuning typically requires 100-1000 annotated images depending on object complexity and domain similarity to COCO.","intents":["adapt DETR to detect custom object classes not in COCO","train on domain-specific data (medical images, aerial photos) with limited annotations","reduce training time and data requirements using transfer learning"],"best_for":["teams with custom detection datasets (100-10k images) who want to leverage pretrained weights","practitioners building domain-specific detectors (medical, industrial, autonomous driving)"],"limitations":["fine-tuning requires careful learning rate scheduling; high LR causes catastrophic forgetting, low LR requires many epochs","bipartite matching loss is sensitive to class imbalance; requires loss weighting for datasets with few instances of rare classes","domain shift from COCO to custom data may require architectural changes (e.g., more queries for crowded scenes)","no built-in data augmentation beyond standard transforms; requires manual augmentation pipeline for small datasets"],"requires":["PyTorch 1.9+","transformers library 4.5.0+","custom dataset in COCO format or compatible annotation format","GPU with 8GB+ VRAM for batch training"],"input_types":["custom dataset annotations in COCO JSON format","image files (JPEG, PNG)"],"output_types":["fine-tuned model checkpoint","training logs with loss curves"],"categories":["image-visual","deep-learning-computer-vision"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebook--detr-resnet-50__cap_7","uri":"capability://image.visual.multi.scale.feature.processing.with.positional.encodings","name":"multi-scale feature processing with positional encodings","description":"Processes CNN features through a transformer encoder that uses positional encodings to inject spatial information into the feature maps. The model uses sine/cosine positional encodings (similar to Vision Transformer) to encode 2D spatial positions, enabling the transformer to reason about object locations without explicit spatial priors. Features are flattened and projected into the transformer embedding space, then processed through multi-head self-attention layers that attend across the entire spatial extent.","intents":["inject spatial information into transformer features without explicit spatial priors","enable transformer attention to reason about object locations and relationships","process variable-resolution features with position-aware attention"],"best_for":["researchers exploring positional encoding strategies for vision transformers","teams building detection models with explicit spatial reasoning"],"limitations":["sine/cosine positional encodings are fixed and not learned; may not be optimal for all spatial distributions","flattening 2D features into 1D sequences loses spatial locality; attention is computed over all positions (O(N²))","positional encodings assume regular grid structure; fails on irregular or sparse features","no multi-scale feature fusion; only single-scale features from ResNet-50 are used"],"requires":["PyTorch 1.9+","transformers library 4.5.0+"],"input_types":["torch.Tensor (B, C, H, W) feature maps from CNN backbone"],"output_types":["torch.Tensor (B, H*W, C) flattened and position-encoded features"],"categories":["image-visual","deep-learning-computer-vision"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+","torchvision with DETR model definitions","transformers library 4.5.0+","CUDA 11.0+ for GPU inference (CPU inference supported but slow)","minimum 4GB VRAM for batch inference","torchvision 0.10.0+","ImageNet normalization stats (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])","PyTorch 1.9+ with CUDA support for efficient attention computation","scipy 1.5.0+ for linear_sum_assignment","PyTorch 1.9+ for gradient computation through matching"],"failure_modes":["slower inference than YOLO variants (~100ms per image on GPU) due to transformer decoder sequential processing","requires fixed input resolution or padding; aspect ratio changes degrade performance","bipartite matching adds computational overhead during training; inference speed not optimized for real-time video (< 30 FPS on consumer GPUs)","struggles with small objects and crowded scenes compared to anchor-based methods due to set prediction formulation","no native support for panoptic segmentation or instance segmentation masks","fixed to ResNet-50 architecture; no option for lighter backbones (ResNet-18) or heavier ones (ResNet-101) in this specific model checkpoint","ImageNet pretraining introduces dataset bias toward natural images; performance degrades on medical, satellite, or synthetic imagery","1/32 spatial resolution may lose fine details for small objects","fixed number of queries (100) means maximum 100 detections per image; sparse scenes waste computation, crowded scenes may miss objects","transformer decoder is autoregressive during training but parallel during inference, creating train-test mismatch","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6544309771591371,"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":239063,"model_likes":947}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=facebook--detr-resnet-50","compare_url":"https://unfragile.ai/compare?artifact=facebook--detr-resnet-50"}},"signature":"HoVSsbIS+Ll6BYN8M5kj6a3Ovrsb/HIbUgL9hNWYhalX6xQP3S5MSK+8glMcmyLHxTG1HmK5zVm+DQOuXMbfCg==","signedAt":"2026-06-22T01:21:55.693Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/facebook--detr-resnet-50","artifact":"https://unfragile.ai/facebook--detr-resnet-50","verify":"https://unfragile.ai/api/v1/verify?slug=facebook--detr-resnet-50","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"}}