Zero-Shot Image Classification
Transformers
PyTorch
OpenCLIP
Safetensors
GGUF
English
clip
Ternary Quantization
Knowledge Distillation
W1.58-A16
Instructions to use zhangsq-nju/TernaryCLIP_ViT-B-16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zhangsq-nju/TernaryCLIP_ViT-B-16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="zhangsq-nju/TernaryCLIP_ViT-B-16") 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("zhangsq-nju/TernaryCLIP_ViT-B-16") model = AutoModelForZeroShotImageClassification.from_pretrained("zhangsq-nju/TernaryCLIP_ViT-B-16") - Notebooks
- Google Colab
- Kaggle
Upload TernaryCLIP_ViT-B-16 model weights
#1
by zhsh17 - opened
No description provided.
zhsh17 changed pull request title from model uploading to Upload TernaryCLIP_ViT-B-16 model weights
zhsh17 changed pull request status to open
zhangsq-nju changed pull request status to merged