Find Everything in Your Image
Detects and localizes multiple objects in photos, returning bounding boxes and confidence scores. Uses transformer architecture (ResNet-50 backbone) to identify what's in your image and where.
What it does
DETR (DEtection TRansformer) finds and identifies objects in images. Point it at any photo, and it returns a list of what it found with precise locations (bounding boxes) and confidence scores. Instead of scanning the image piece-by-piece like older methods, DETR uses transformers to understand the entire image at once and directly predict all objects. Can detect 80+ object types: people, cars, dogs, chairs, cups, backpacks, and much more.
Problem it solves
- Detect multiple objects in a single image (people, animals, vehicles, furniture, etc.)
- Localize objects with precise bounding boxes showing where each object is
- Real-time object detection for video surveillance, monitoring, autonomous systems
- Visual understanding for image search, content moderation, accessibility
- End-to-end pipeline fully trainable (no hand-crafted region proposals)
- General-purpose detection across 80+ object categories
- Batch processing for efficient large-scale image analysis
Input/Output
- Input: RGB image
- Output: List of detected objects
- Class label: What the object is (e.g., "person", "dog", "car", "chair")
- Bounding box: Location in image [x_min, y_min, x_max, y_max]
- Confidence score: How sure (0.0–1.0, where 1.0 = 100% sure)
Accuracy
- Quality by Object Type:
- Large, distinct objects: Excellent detection (90%+ confidence)
- Medium objects: Very good detection (70-85% confidence)
- Small objects: Reasonable detection (50-70% confidence)
- Occluded objects: May miss or detect as separate objects
- Crowded scenes: Good, handles multiple overlapping objects
Technical Details
Architecture | Encoder-Decoder Transformer (DETR) |
Core Innovation | Treats object detection as direct set prediction instead of region-based detection. Uses 100 learnable "object queries" that simultaneously search for objects and predict their classes and locations. |
Components | • CNN Backbone (ResNet-50): Extracts multi-scale image features
• Positional Encoding: Encodes spatial coordinates for the transformer
• Transformer Encoder: Processes global image context via multi-head attention
• Transformer Decoder: 100 object queries with cross-attention to encoder features
• Prediction Heads: Classification (80 COCO classes) and bounding box regression (x, y, w, h) |
Training | • Dataset: COCO 2017 (80 object categories)
• Loss: Bipartite matching (Hungarian algorithm) + cross-entropy classification + L1 + Generalized IoU localization
• Optimizer: AdamW over 150 epochs with learning rate decay |
Requirements | • PyTorch (v1.7+)
• GPU recommended for inference |
Compliance & Provenance
Provider | Open-source |
Provider type | Specialized |
License | Apache 2.0 |
EU AI Act risk class | Minimal Risk |
Art. 50 transparency | Not applicable |
Region availability | Available globally |
Training data summary | Pending — provider has not yet published per Art. 53(d) |
For more on how we classify models and mark outputs, see our AI Policy.
Model Source
- HuggingFace: https://huggingface.co/facebook/detr-resnet-50
- GitHub Repository: https://github.com/facebookresearch/detr
- Paper: "End-to-End Object Detection with Transformers" Carion et al., ECCV 2020 https://arxiv.org/pdf/2005.12872
- License: apache-2.0