google/vit-base-patch16-384 cover image

google/vit-base-patch16-384

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.

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.

Public
$0.0005 / sec

HTTP/cURL API

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

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:

{
  "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
  }
}

Input fields

imagestring

image to classify


webhookfile

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

Input Schema

Output Schema