microsoft/beit-base-patch16-224-pt22k-ft22k
The BEiT model is a Vision Transformer (ViT) pre-trained on ImageNet-21k, a dataset of 14 million images and 21,841 classes, using a self-supervised approach. The model was fine-tuned on the same dataset and achieved state-of-the-art performance on various image classification benchmarks. The BEiT model uses relative position embeddings and mean-pools the final hidden states of the patch embeddings for classification.
The BEiT model is a Vision Transformer (ViT) pre-trained on ImageNet-21k, a dataset of 14 million images and 21,841 classes, using a self-supervised approach. The model was fine-tuned on the same dataset and achieved state-of-the-art performance on various image classification benchmarks. The BEiT model uses relative position embeddings and mean-pools the final hidden states of the patch embeddings for classification.
HTTP/cURL API
You can use cURL or any other http client to run inferences:
curl -X POST \
-H "Authorization: bearer $DEEPINFRA_TOKEN" \
-F image=@my_image.jpg \
'https://api.deepinfra.com/v1/inference/microsoft/beit-base-patch16-224-pt22k-ft22k'
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
webhookfile
The webhook to call when inference is done, by default you will get the output in the response of your inference request
