openai/clip-vit-base-patch32
The CLIP model was developed by OpenAI to investigate the robustness of computer vision models. It uses a Vision Transformer architecture and was trained on a large dataset of image-caption pairs. The model shows promise in various computer vision tasks but also has limitations, including difficulties with fine-grained classification and potential biases in certain applications.
The CLIP model was developed by OpenAI to investigate the robustness of computer vision models. It uses a Vision Transformer architecture and was trained on a large dataset of image-caption pairs. The model shows promise in various computer vision tasks but also has limitations, including difficulties with fine-grained classification and potential biases in certain applications.
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 \
-F 'candidate_labels=["cat","dog"]' \
'https://api.deepinfra.com/v1/inference/openai/clip-vit-base-patch32'
which will give you back something similar to:
{
"results": [
{
"label": "dog",
"score": 0.9
},
{
"label": "cat",
"score": 0.1
}
],
"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
Input Schema
Output Schema
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