Image Feature Extraction
Transformers
JAX
Safetensors
MLX
PyTorch
aimv2_vision_model
vision
custom_code
Eval Results (legacy)
Instructions to use apple/aimv2-large-patch14-336 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/aimv2-large-patch14-336 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="apple/aimv2-large-patch14-336", trust_remote_code=True)# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("apple/aimv2-large-patch14-336", trust_remote_code=True) model = AutoModel.from_pretrained("apple/aimv2-large-patch14-336", trust_remote_code=True, device_map="auto") - MLX
How to use apple/aimv2-large-patch14-336 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir aimv2-large-patch14-336 apple/aimv2-large-patch14-336
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
- Xet hash:
- b5893da8284509243e8177542d6a7caa2e785b9a7bb0bcdedb4ad1640f5c0818
- Size of remote file:
- 1.24 GB
- SHA256:
- 669deea9493c13b8d86e7ea96bd8b2fcfdaa35e904830868a45a9518c0a763a6
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