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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.
Parameters6M
Context—
Weights52.0 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads167.4k
Runs On
What it takes to serve yolos-tiny (6M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
Model Card
By HUST Vision Lab, published under apache-2.0, revision 95a90f3c189f.
YOLOS (tiny-sized) model
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.
Model description
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).
Configuration
- Architecture
- YolosForObjectDetection
- Layers
- 12
- Hidden size
- 192
- Feed-forward size
- 768
- Attention heads
- 3
- Stored precision
- float32
- Model type
- yolos
Identity and Version
- Repository
- hustvl/yolos-tiny
- Publisher
- HUST Vision Lab
- Task
- Object detection
- Modality
- Image
- Library
- transformers
- Parameters
- 6M parameters
- Languages
- Not stated by the source
- Revision
- 95a90f3c189fbfca3bcfc6d7315b9e84d95dc2de
- First published
- 2022-04-26
- Last updated
- 2024-04-10
Files and Weights
6 files, 52.0 MB in total. The weights are 2 files totalling 52.0 MB in bin, safetensors.
Weights2 files · 52.0 MB
Configuration2 files · 4.4 KB
Documentation1 file · 4.6 KB
Repository1 file · 1.2 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 26.0 MB | 5a6a017a20cb |
| pytorch_model.bin | Weights | 26.0 MB | 6a5cc7772832 |
| config.json | Configuration | 4.1 KB | — |
| preprocessor_config.json | Configuration | 291 B | — |
| README.md | Documentation | 4.6 KB | — |
| .gitattributes | Repository | 1.2 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 52.0 MB
Download from HUST Vision Lab
Released by HUST Vision Lab through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2106.00666
- Trained on (disclosed) coco
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 52.0 MB |
| 16-bit | 0.0 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Built on This Model
- Quantized fromyolos-tiny
- Derived fromyolos-tiny
Questions About yolos-tiny
How much GPU memory does yolos-tiny need?
About 0 GB at 16-bit and 0 GB at 4-bit: the weights (6M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run yolos-tiny on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use yolos-tiny commercially?
Yes. yolos-tiny is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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