The Vision Transformer (ViT) model, pre-trained on ImageNet-21k and fine-tuned on ImageNet, achieves state-of-the-art results on image classification tasks. The model uses a transformer encoder architecture and presents images as a sequence of fixed-size patches, adding a [CLS] token for classification tasks. The pre-trained model can be used for downstream tasks such as extracting features and training standard classifiers.

You can use cURL or any other http client to run inferences:

```bash
curl -X POST \
    -H "Authorization: bearer $(deepctl auth token)"  \
    -F image=@my_image.jpg  \
    'https://api.deepinfra.com/v1/inference/google/vit-base-patch16-384'
```

which will give you back something similar to:

```json
{
  "results": [
    {
      "label": "Maltese dog, Maltese terrier, Maltese",
      "score": 0.9235488176345825
    },
    {
      "label": "Lhasa, Lhasa apso",
      "score": 0.0298430435359478
    }
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

```


You can use our command-line tool [deepctl](/docs/getting-started) to run
inferences:

```bash
deepctl infer \
    -m 'google/vit-base-patch16-384'  \
    -i image=@my_image.jpg
```

which will give you back something similar to:

```json
{
  "results": [
    {
      "label": "Maltese dog, Maltese terrier, Maltese",
      "score": 0.9235488176345825
    },
    {
      "label": "Lhasa, Lhasa apso",
      "score": 0.0298430435359478
    }
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

```


image

The webhook to call when inference is done, by default you will get the output in the response of your inference request

google/vit-base-patch16-384

Input

Output