detr-resnet-50
by facebook
End-to-end object detection with Transformers, no anchor boxes needed
facebook/detr-resnet-50mixpeek://image_extractor@v1/facebook_detr_r50_v1Overview
DETR (DEtection TRansformer) reimagines object detection as a set prediction problem, using a transformer encoder-decoder architecture to directly output a set of bounding boxes and class labels without the need for hand-designed components like anchor boxes or non-maximum suppression.
On Mixpeek, DETR extracts structured object annotations from video frames and images, producing bounding boxes with class labels that power attribute-based filtering in retrieval pipelines.
Architecture
ResNet-50 CNN backbone followed by a 6-layer transformer encoder-decoder. Uses bipartite matching loss (Hungarian algorithm) to assign predictions to ground truth. Outputs 100 object queries in parallel.
Mixpeek SDK Integration
// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so detr-resnet-50 runs
// on your side and the output is upserted through POST
// /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
// path is to upload the weights instead: POST /v1/namespaces/{id}/models
// accepts the huggingface format and a custom plugin loads them.
const res = await fetch(
"https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
{
method: "POST",
headers: {
Authorization: "Bearer API_KEY",
"Content-Type": "application/json",
},
body: JSON.stringify({
collection_id: "col_your_collection",
documents: [
{
document_id: "asset-00412",
// Boxes, masks, depth maps and anomaly scores are structured
// results, not vectors. They go in payload and are reachable
// through pre_filters on a retriever, not through similarity.
payload: {
detections: modelOutput,
source_key: "archive/2026/asset-00412",
},
},
],
}),
},
);
// No managed alternative for an open label set. Two extractors do emit a
// bbox, for the one thing each detects: document_graph_extractor@v1 per
// layout block, face_identity_extractor@v1 per face. Nothing ships that
// returns masks, depth maps or anomaly scores.Capabilities
- 91 COCO object categories out of the box
- Bounding box + class label predictions
- Panoptic segmentation with extensions
- No hand-designed post-processing (NMS-free)
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| COCO val2017 | AP (box) | 42.0 | Carion et al., 2020: Table 1 |
| COCO val2017 | AP50 | 62.4 | Carion et al., 2020: Table 1 |
| COCO val2017 | AP (small) | 20.5 | Carion et al., 2020: Table 1 |
Performance
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Specification
Research Paper
End-to-End Object Detection with Transformers
arxiv.orgBuild a pipeline with detr-resnet-50
Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.
Run it on your own data, free