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rcfg
/
FashionBLIP-1

Image-Text-to-Text
Transformers
Safetensors
blip
vision
image-captioning
multimodal
fashion
Model card Files
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 Community
How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="rcfg/FashionBLIP-1")
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("rcfg/FashionBLIP-1")
model = AutoModelForMultimodalLM.from_pretrained("rcfg/FashionBLIP-1", device_map="auto")
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Fine-Tuned BLIP Model for Fashion Image Captioning

This is a fine-tuned BLIP (Bootstrapped Language-Image Pretraining) model specifically designed for fashion image captioning. It was fine-tuned on the Marqo Fashion Dataset to generate descriptive and contextually relevant captions for fashion-related images.

Model Details

  • Model Type: BLIP (Vision-Language Pretraining)
  • Architecture: BLIP uses a multimodal transformer architecture to jointly model visual and textual information.
  • Fine-Tuning Dataset: Marqo Fashion Dataset (a dataset containing fashion images and corresponding captions)
  • Task: Fashion Image Captioning
  • License: Apache 2.0

Usage

You can use this model with the Hugging Face transformers library for fashion image captioning tasks.

Installation

First, install the required libraries:

pip install transformers torch
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Dataset used to train rcfg/FashionBLIP-1