Image Feature Extraction
Transformers
JAX
Safetensors
MLX
PyTorch
aimv2_vision_model
vision
custom_code
Eval Results (legacy)
Instructions to use apple/aimv2-huge-patch14-336 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/aimv2-huge-patch14-336 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="apple/aimv2-huge-patch14-336", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("apple/aimv2-huge-patch14-336", trust_remote_code=True) model = AutoModel.from_pretrained("apple/aimv2-huge-patch14-336", trust_remote_code=True, device_map="auto") - MLX
How to use apple/aimv2-huge-patch14-336 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download apple/aimv2-huge-patch14-336 --local-dir aimv2-huge-patch14-336
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download preprocessor_config.json from apple/aimv2-huge-patch14-336: direct link, hf CLI and curl.
- Browser
- Download file 636 Bytes
-
https://huggingface.co/apple/aimv2-huge-patch14-336/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://apple/aimv2-huge-patch14-336/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/apple/aimv2-huge-patch14-336/resolve/main/preprocessor_config.json
636 Bytes
| { | |
| "crop_size": { | |
| "height": 336, | |
| "width": 336 | |
| }, | |
| "data_format": "channels_first", | |
| "default_to_square": false, | |
| "device": null, | |
| "disable_grouping": null, | |
| "do_center_crop": true, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "CLIPImageProcessorFast", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "input_data_format": null, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_tensors": null, | |
| "size": { | |
| "shortest_edge": 336 | |
| } | |
| } | |