Image Classification
Transformers
Safetensors
English
custom_vit_nano
vit
nano
patch16
img224
custom_code
Instructions to use kd13/vit-nano-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kd13/vit-nano-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kd13/vit-nano-patch16-224", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("kd13/vit-nano-patch16-224", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +3 -3
config.json
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{
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"architectures": [
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"
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],
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"auto_map": {
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"AutoConfig": "configuration_vit.
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"AutoModelForImageClassification": "modeling_vit.
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},
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"model_type": "custom_vit_nano",
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"image_size": 224,
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{
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"architectures": [
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"CustomViTNanoForImageClassification"
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],
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"auto_map": {
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"AutoConfig": "configuration_vit.CustomViTNanoConfig",
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"AutoModelForImageClassification": "modeling_vit.CustomViTNanoForImageClassification"
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},
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"model_type": "custom_vit_nano",
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"image_size": 224,
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