Zero-Shot Image Classification
Transformers
Safetensors
English
clip
fashion
multimodal
image-search
text-search
embeddings
contrastive-learning
zero-shot-classification
Instructions to use Leacb4/gap-clip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Leacb4/gap-clip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Leacb4/gap-clip") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("Leacb4/gap-clip") model = AutoModelForZeroShotImageClassification.from_pretrained("Leacb4/gap-clip") - Notebooks
- Google Colab
- Kaggle
File size: 1,634 Bytes
fac3f86 01ca95a 38783e1 01ca95a fac3f86 38783e1 fac3f86 38783e1 fac3f86 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | """
GAP-CLIP: Guaranteed Attribute Positioning in CLIP Embeddings
==============================================================
A multimodal fashion search model that combines color embeddings,
hierarchical category embeddings, and general CLIP capabilities.
Main Components:
- ColorCLIP: Specialized color embedding model (16 dims)
- HierarchyModel: Category classification model (64 dims)
- GAP-CLIP: Main CLIP model with aligned subspaces (512 dims)
Quick Start:
>>> from gap_clip import load_models_from_hf
>>> models = load_models_from_hf("Leacb4/gap-clip")
>>> # Use models for search...
For more information, see the README.md file or visit:
https://huggingface.co/Leacb4/gap-clip
"""
__version__ = "1.0.0"
__author__ = "Lea Attia Sarfati"
__email__ = "lea.attia@gmail.com"
# Import main components for easy access
try:
from .training.color_model import ColorCLIP
from .training.hierarchy_model import HierarchyModel, HierarchyExtractor
from .example_usage import (
load_gap_clip, get_image_embedding_from_url, get_text_embedding,
load_models_from_hf, load_models_from_local, example_search,
)
from . import config
__all__ = [
'ColorCLIP',
'HierarchyModel',
'HierarchyExtractor',
'load_gap_clip',
'get_image_embedding_from_url',
'get_text_embedding',
'load_models_from_hf',
'load_models_from_local',
'example_search',
'config',
'__version__',
]
except ImportError:
# If imports fail, it's ok - the package can still be used
__all__ = ['__version__']
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