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…
Open-weight model · Object detection
pothole_detection
by Atharv Patawar savioratharv/pothole_detection
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model.
Parameters6M
Context—
Weights25.9 MB
License—
AccessOpen weights
Monthly Downloads5.3k
Runs On
What it takes to serve pothole_detection (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
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Excerpt from the card by Atharv Patawar.
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
- savioratharv/pothole_detection
- Publisher
- Atharv Patawar
- Task
- Object detection
- Modality
- Image
- Library
- transformers
- Parameters
- 6M parameters
- Languages
- Not stated by the source
- Revision
- 07ab97e0d3e434322cae2b9f09398216b37274ea
- First published
- 2024-01-31
- Last updated
- 2024-02-04
Files and Weights
5 files, 25.9 MB in total. The weights are 1 file totalling 25.9 MB in safetensors.
Weights1 file · 25.9 MB
Configuration2 files · 1.2 KB
Documentation1 file · 5.2 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 25.9 MB | 0d1f4e95ff09 |
| config.json | Configuration | 783 B | — |
| preprocessor_config.json | Configuration | 457 B | — |
| README.md | Documentation | 5.2 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 25.9 MB
Download from Atharv Patawar
Released by Atharv Patawar through its official repository on Hugging Face.
Built From
- Described by arXiv:1910.09700
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 25.9 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.
Questions About pothole_detection
How much GPU memory does pothole_detection 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 pothole_detection 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.
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