ModelListedExternal

BLIP Image To Recipe

import requests from PIL import Image

Sourcehuggingface.co/fatehmujtaba/BLIP-Image-to-recipe/tree/b206555b13f709db7b8fc5d39b0b5432d8df0965

KindText modelVersionmodelCgrade

Model source

Kind: Text model
File size: 944 MB
Version: vmodel
Updated: Oct 7, 2026
Downloads: 5
Source: Hugging Face

Source description

BLIP-Image-to-recip

Inference code

import requests from PIL import Image

from transformers import BlipForConditionalGeneration, AutoProcessor

img_url = 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSQuFg4LTHUattLGPU0kLzYpBGHRtuqgJY8Gho3uZe_cg&s' image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')

model = BlipForConditionalGeneration.from_pretrained("Fatehmujtaba/BLIP-Image-to-recipe").to(device) processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")

inputs = processor(images=image, return_tensors="pt").to(device) pixel_values = inputs.pixel_values generated_ids = model.generate(pixel_values=pixel_values, max_length=50) generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

Sources

1 source

Hugging FacePrimaryVersion b206555b13f709db7b8fc5d39b0b5432d8df0965

huggingface.co/fatehmujtaba/BLIP-Image-to-recipe/tree/b206555b13f709db7b8fc5d39b0b5432d8df0965

Verified Oct 7

Model artifacts

1 artifact

model.safetensors

safetensors · 944 MB · SHA-256 028246ed7d2f…c43c · Hugging Face

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Source excerpts

3 excerpts

ReadmeJul 30huggingface.co/fatehmujtaba/BLIP-Image-to-recipe

--- library_name: transformers license: apache-2.0 pipeline_tag: image-to-text --- # BLIP-Image-to-recip # Inference code --- import requests from PIL import Image from transformers import BlipForConditionalGeneration, AutoProcessor img_url = 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSQuFg4LTHUattLGPU0kLzYpBGHRtuqgJY8Gho3uZe_cg&s' image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') model = BlipForConditionalGeneration.from_pretrained("Fatehmujtaba/BLIP-Image-to-recipe").to(device) processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") inputs = processor(images=image, return_tensors="pt").to(device) pixel_values = inputs.pixel_values generated_ids = model.generate(pixel_values=pixel_values, max_length=50) generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] ---

NoteOct 7huggingface.co/fatehmujtaba/BLIP-Image-to-recipe

Source context: 5 downloads · 0 likes · Pipeline image-to-text · Library transformers · Repo fatehmujtaba/BLIP-Image-to-recipe

NoteJul 30huggingface.co/fatehmujtaba/BLIP-Image-to-recipe

Source context: 4 downloads · 0 likes · Pipeline image-to-text · Library transformers · Repo fatehmujtaba/BLIP-Image-to-recipe