ModelPublicadoExterno

Blip Flickr8k Caption

Fine-tuned BLIP image captioning model trained on Flickr8k.

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

TipoText modelVersiónmodelCcalidad

Fuente del modelo

Tipo: Text model
Tamaño del archivo: 944 MB
Versión: vmodel
Actualizado: 8 mar 2026
Descargas: 3
Fuente: Hugging Face

Descripción de la fuente

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)

Fuentes

1 fuente

Hugging FacePrincipalVersión e59afdc652d5e24385ea8b3101e2779090f97539

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

Verificado 3 sept

Artefactos del modelo

1 artefacto

model.safetensors

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

Descargar

Extractos de fuentes

2 extractos

Readme3 septhuggingface.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)