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Fork of openai/clip-vit-base-patch32 for a zero-sho-image-classification Inference endpoint.

This repository implements a custom task for zero-shot-image-classification for 🤗 Inference Endpoints. The code for the customized pipeline is in the pipeline.py.

To use deploy this model a an Inference Endpoint you have to select Custom as task to use the pipeline.py file. -> double check if it is selected

expected Request payload

  "image": "/9j/4AAQSkZJRgABAQEBLAEsAAD/2wBDAAMCAgICAgMC....", // base64 image as bytes

below is an example on how to run a request using Python and requests.

Run Request

  1. prepare an image.
!wget https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
  1. run request
import json
from typing import List
import requests as r
import base64


def predict(path_to_image: str = None, candiates: List[str] = None):
    with open(path_to_image, "rb") as i:
        b64 = base64.b64encode(i.read())

    payload = {"inputs": {"image": b64.decode("utf-8"), "candiates": candiates}}
    response = r.post(
        ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json=payload
    return response.json()

prediction = predict(
    path_to_image="palace.jpg", candiates=["sea", "palace", "car", "ship"]

expected output

[{'label': 'palace', 'score': 0.9996134638786316},
 {'label': 'car', 'score': 0.0002602009626571089},
 {'label': 'ship', 'score': 0.00011758189066313207},
 {'label': 'sea', 'score': 8.666840585647151e-06}]
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Space using philschmid/clip-zero-shot-image-classification 1