Instructions to use rakeshjv2000/fashion-clip-vit-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rakeshjv2000/fashion-clip-vit-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="rakeshjv2000/fashion-clip-vit-base-patch32") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("rakeshjv2000/fashion-clip-vit-base-patch32") model = AutoModelForZeroShotImageClassification.from_pretrained("rakeshjv2000/fashion-clip-vit-base-patch32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from rakeshjv2000/fashion-clip-vit-base-patch32: direct link, hf CLI and curl.
- Browser
- Download file 625 Bytes
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https://huggingface.co/rakeshjv2000/fashion-clip-vit-base-patch32/resolve/main/README.md
- Command line
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hf download hf://rakeshjv2000/fashion-clip-vit-base-patch32/README.md
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curl -L -o README.md https://huggingface.co/rakeshjv2000/fashion-clip-vit-base-patch32/resolve/main/README.md
625 Bytes
metadata
language: en
license: mit
tags:
- clip
- vision
- image-text
- fashion
- transformers
library_name: transformers
pipeline_tag: zero-shot-image-classification
Fashion CLIP ViT-B/32
Fine-tuned CLIP model for fashion image-text retrieval.
Model Details
- Base model: openai/clip-vit-base-patch32
- Fine-tuned on a fashion dataset
- Task: image-text similarity & retrieval
Usage
from transformers import CLIPModel, CLIPProcessor
model = CLIPModel.from_pretrained("rakeshjv2000/fashion-clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("rakeshjv2000/fashion-clip-vit-base-patch32")