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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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How to use from
vLLM
Install from pip and serve model 
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rcfg/FashionBLIP-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rcfg/FashionBLIP-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker 
docker model run hf.co/rcfg/FashionBLIP-1
Quick Links

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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