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 π€ 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
"candiates":["sea","palace","car","ship"]
}
below is an example on how to run a request using Python and requests
.
Run Request
- prepare an image.
!wget https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
- run request
import json
from typing import List
import requests as r
import base64
ENDPOINT_URL = ""
HF_TOKEN = ""
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}]
- Downloads last month
- 36
Inference API (serverless) does not yet support generic models for this pipeline type.