Image Feature Extraction
Transformers
JAX
Safetensors
MLX
PyTorch
aimv2_vision_model
vision
custom_code
Instructions to use apple/aimv2-large-patch14-224-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/aimv2-large-patch14-224-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="apple/aimv2-large-patch14-224-distilled", trust_remote_code=True)# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("apple/aimv2-large-patch14-224-distilled", trust_remote_code=True) model = AutoModel.from_pretrained("apple/aimv2-large-patch14-224-distilled", trust_remote_code=True) - MLX
How to use apple/aimv2-large-patch14-224-distilled with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir aimv2-large-patch14-224-distilled apple/aimv2-large-patch14-224-distilled
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Xet hash:
- 0802f7577f29285afbf08481cafd61068b774e19c2921f03302d85469ae90b4a
- Size of remote file:
- 1.24 GB
- SHA256:
- 15a2c2ac9343b9ae090ef7a7e88f1367fcc12cbb6f39cd96584f620b12a00061
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