Zero-Shot Image Classification
Transformers
JAX
Safetensors
MLX
PyTorch
aimv2
feature-extraction
vision
custom_code
Instructions to use apple/aimv2-large-patch14-224-lit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/aimv2-large-patch14-224-lit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="apple/aimv2-large-patch14-224-lit", trust_remote_code=True) 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, AutoModel processor = AutoProcessor.from_pretrained("apple/aimv2-large-patch14-224-lit", trust_remote_code=True) model = AutoModel.from_pretrained("apple/aimv2-large-patch14-224-lit", trust_remote_code=True) - MLX
How to use apple/aimv2-large-patch14-224-lit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir aimv2-large-patch14-224-lit apple/aimv2-large-patch14-224-lit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Xet hash:
- 837a9f422596efca67c1d1532516982126bed39b7ab9e0055843bb8d5ad959a3
- Size of remote file:
- 1.75 GB
- SHA256:
- e9a0949dabf81c8c75891cef7cb624140b82570da0922cd190926dd623e62194
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