Image Classification
timm
Safetensors
vision-transformer
adaptive-inference
elastic-inference
imagenet-1k
Instructions to use NCPS/thinkingvit_deit-3h-6h-800epochs-imagenet1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use NCPS/thinkingvit_deit-3h-6h-800epochs-imagenet1k with timm:
import timm model = timm.create_model("hf_hub:NCPS/thinkingvit_deit-3h-6h-800epochs-imagenet1k", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Add ThinkingViT weights
Browse files
README.md
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- accuracy
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---
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# ThinkingViT
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This repository contains the ImageNet-1K EMA weights for **ThinkingViT
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[ThinkingViT: Matryoshka Thinking Vision Transformer for Elastic Inference](https://arxiv.org/abs/2507.10800).
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- Paper: https://arxiv.org/abs/2507.10800
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from timm.models import create_model
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# Run from the ThinkingViT repository root, or put this repository on PYTHONPATH.
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model = create_model("hf-hub:NCPS/
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model.eval()
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x = torch.randn(1, 3, 224, 224)
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- accuracy
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---
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# ThinkingViT DeiT 3H -> 6H 800 Epochs ImageNet-1K
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This repository contains the ImageNet-1K EMA weights for **ThinkingViT DeiT 3H -> 6H 800 Epochs ImageNet-1K** from
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[ThinkingViT: Matryoshka Thinking Vision Transformer for Elastic Inference](https://arxiv.org/abs/2507.10800).
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- Paper: https://arxiv.org/abs/2507.10800
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from timm.models import create_model
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# Run from the ThinkingViT repository root, or put this repository on PYTHONPATH.
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model = create_model("hf-hub:NCPS/thinkingvit_deit-3h-6h-800epochs-imagenet1k", pretrained=True)
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model.eval()
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x = torch.randn(1, 3, 224, 224)
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