hustvl/yolos-base
object-detection model by hustvl
From the publisher
Model card & documentation
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YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection by Fang et al. and first released in this repository. Disclaimer: The team releasing YOLOS did not write a model card for this model so this model card has been written by the Hugging Face team. YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN). The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4…
Read the full model card ↗ · Preview checked 2026-10-28T02:56:51.503Z
Inside the original model card — Document outline
- YOLOS (base-sized) model
- Model description
- Intended uses & limitations
- How to use
- Training data
- Training
- Evaluation results
- BibTeX entry and citation info
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Model overview
hustvl/yolos-base is a object-detection model repository published by hustvl on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- object-detection
- Library
- transformers
- Recent downloads (30 days)
- 6,931
- Cumulative likes
- 28
- Hugging Face trending score
- 0
- Reported safetensors parameters
- Not reported
- Architecture
- YolosForObjectDetection
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2022-04-26T09:30:39.000Z
- Last modified
- 2024-05-08T07:48:46.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:54:34.007Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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