yolos-tiny
by hustvl
You Only Look at One Sequence, ViT-based real-time object detection
hustvl/yolos-tinymixpeek://image_extractor@v1/hustvl_yolos_tiny_v1Overview
YOLOS adapts the Vision Transformer (ViT) architecture for object detection by simply appending detection tokens to the input sequence. It demonstrates that a pure transformer can perform object detection without any convolutional components.
On Mixpeek, YOLOS Tiny provides a lightweight, fast alternative to DETR for object detection tasks where speed is prioritized over maximum accuracy.
Architecture
Vision Transformer (ViT-Tiny) with 12 layers. Appends 100 learnable detection tokens to the image patch sequence. Uses bipartite matching loss like DETR.
Mixpeek SDK Integration
// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so yolos-tiny 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
- Lightweight ViT-based object detection
- Fast inference suitable for real-time processing
- COCO object categories
- Pure transformer architecture (no CNN backbone)
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| COCO val2017 | AP (box) | 30.4 | Fang et al., 2021: Table 1 |
| COCO val2017 | AP50 | 48.6 | Fang et al., 2021: Table 1 |
Performance
6.5M params: optimized for edge and high-throughput scenarios
Common Pipeline Companions
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Specification
Research Paper
You Only Look at One Sequence
arxiv.orgBuild a pipeline with yolos-tiny
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