ModelListedExternal

Blip Flickr8k Caption

Fine-tuned BLIP image captioning model trained on Flickr8k.

Sourcehuggingface.co/muhammedaydiiinnn/blip-flickr8k-caption/tree/e59afdc652d5e24385ea8b3101e2779090f97539

KindText modelVersionmodelCgrade

Model source

Kind: Text model
File size: 944 MB
Version: vmodel
Updated: Sep 18, 2026
Downloads: 3
Source: Hugging Face

Source description

BLIP Flickr8k Caption Model

Fine-tuned BLIP image captioning model trained on Flickr8k.

Base model

Salesforce/blip-image-captioning-base

Usage

from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image
import torch

model_id = "muhammedaydiiinnn/blip-flickr8k-caption"

processor = BlipProcessor.from_pretrained(model_id)
model = BlipForConditionalGeneration.from_pretrained(model_id)

image = Image.open("test.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")

with torch.inference_mode():
    output = model.generate(**inputs, max_new_tokens=30)

caption = processor.decode(output[0], skip_special_tokens=True)
print(caption)

Sources

1 source

Hugging FacePrimaryVersion e59afdc652d5e24385ea8b3101e2779090f97539

huggingface.co/muhammedaydiiinnn/blip-flickr8k-caption/tree/e59afdc652d5e24385ea8b3101e2779090f97539

Verified Sep 3

Model artifacts

1 artifact

model.safetensors

safetensors · 944 MB · SHA-256 6e023c44a52a…1015 · Hugging Face

Download file

Source excerpts

2 excerpts

ReadmeSep 3huggingface.co/muhammedaydiiinnn/blip-flickr8k-caption

--- language: - en library_name: transformers pipeline_tag: image-to-text tags: - image-captioning - blip - vision-language license: mit --- # BLIP Flickr8k Caption Model Fine-tuned BLIP image captioning model trained on Flickr8k. ## Base model Salesforce/blip-image-captioning-base ## Usage ```python from transformers import BlipProcessor, BlipForConditionalGeneration from PIL import Image import torch model_id = "muhammedaydiiinnn/blip-flickr8k-caption" processor = BlipProcessor.from_pretrained(model_id) model = BlipForConditionalGeneration.from_pretrained(model_id) image = Image.open("test.jpg").convert("RGB") inputs = processor(images=image, return_tensors="pt") with torch.inference_mode(): output = model.generate(**inputs, max_new_tokens=30) caption = processor.decode(output[0], skip_special_tokens=True) print(caption)

NoteSep 3huggingface.co/muhammedaydiiinnn/blip-flickr8k-caption

Source context: 3 downloads · 0 likes · Pipeline image-to-text · Library transformers · Repo muhammedaydiiinnn/blip-flickr8k-caption