# Is there any full Vision transformer model for object detection?

> Source: <https://discuss.huggingface.co/t/is-there-any-full-vision-transformer-model-for-object-detection/179750#post_2>
> Published: 2026-09-02 10:01:56+00:00

Probably, yes:

If by “full Vision Transformer” you mean **an object detector whose main visual backbone is a ViT rather than a CNN**, there are several examples now.

For the specific shape you mentioned — **something like a plain ViT backbone + a DETR-style head/decoder** — I would start with ** LW-DETR**. Hugging Face describes its architecture as a

If you want a newer, practical detector built around a pretrained ViT backbone, ** RF-DETR** is also worth looking at. It uses a

And if what you mean is closer to **“vanilla ViT itself turned into an object detector”**, then ** YOLOS** is probably the cleanest reference point. It modifies ViT only minimally and adds detection tokens/heads rather than attaching a conventional DETR encoder-decoder stack.

So, roughly:

| What you are looking for | Candidate | Transformers usability |
|---|---|---|
| plain ViT + DETR-style decoder |
|

`pipeline`

/ `AutoModelForObjectDetection`

`pipeline`

/ `AutoModelForObjectDetection`

The only thing I would be careful about is the word **“entirely”**, because there are a few different meanings hiding inside it.

If I were choosing where to start:

```
Want the closest thing to "plain ViT backbone + DETR head"?
    → LW-DETR

Want a newer practical ViT-based detector with good current HF support?
    → RF-DETR

Want the cleanest vanilla-ViT-as-detector reference?
    → YOLOS

Want to build a ViT backbone + strong DETR-family detector yourself?
    → ViTDet + DINO/detrex

Want specifically "no CNN backbone" as a research constraint?
    → WB-DETR

Want literally zero Conv2d / convolution operations?
    → treat that as a separate requirement and audit the exact model implementation
```

So the answer is definitely not “there is no full-ViT object detector”; rather, there are several families already. The main choice is **what you mean by “full”** and whether your priority is architectural purity, a DETR-like component boundary, current Transformers usability, or practical detection performance.