Image Feature Extraction
Transformers
JAX
Safetensors
MLX
PyTorch
aimv2_vision_model
vision
custom_code
Eval Results (legacy)
Instructions to use apple/aimv2-large-patch14-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/aimv2-large-patch14-224 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", trust_remote_code=True)# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("apple/aimv2-large-patch14-224", trust_remote_code=True) model = AutoModel.from_pretrained("apple/aimv2-large-patch14-224", trust_remote_code=True) - MLX
How to use apple/aimv2-large-patch14-224 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir aimv2-large-patch14-224 apple/aimv2-large-patch14-224
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
File size: 728 Bytes
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"architectures": [
"AIMv2Model"
],
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_aimv2.AIMv2Config",
"AutoModel": "modeling_aimv2.AIMv2Model",
"FlaxAutoModel": "modeling_flax_aimv2.FlaxAIMv2Model"
},
"hidden_act": "silu",
"hidden_size": 1024,
"image_size": 224,
"initializer_range": 0.02,
"intermediate_size": 2816,
"is_native": false,
"mlp_bias": false,
"model_type": "aimv2",
"num_attention_heads": 8,
"num_channels": 3,
"num_hidden_layers": 24,
"patch_size": 14,
"projection_dropout": 0.0,
"qkv_bias": false,
"rms_norm_eps": 1e-05,
"torch_dtype": "float32",
"transformers_version": "4.54.0.dev0",
"use_bias": false,
"use_head": false
}
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