Instructions to use timm/vit_dwee_patch16_reg1_gap_256.sbb_nadamuon_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_dwee_patch16_reg1_gap_256.sbb_nadamuon_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/vit_dwee_patch16_reg1_gap_256.sbb_nadamuon_in1k", pretrained=True) - Transformers
How to use timm/vit_dwee_patch16_reg1_gap_256.sbb_nadamuon_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/vit_dwee_patch16_reg1_gap_256.sbb_nadamuon_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_dwee_patch16_reg1_gap_256.sbb_nadamuon_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update model config and README
Browse files
README.md
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| pwee | 256 | 1280 (5) | 4 | 16 (parallel) | y |
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| dpwee | 256 | 1280 (5) | 4 | 16 (parallel + differential) | y |
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| small | 384 | 1536 (4) | 6 | 12 | n |
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| little | 320 | 1792 (5.6) | 5 | 14
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| dlittle | 320 | 1792 (5.6) | 5 | 14 | y |
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| medium | 512 | 2048 (4) | 8 | 12 | y |
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| mediumd | 512 | 2048 (4) | 8 | 20 | y |
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| betwixt | 640 | 2560 (4) | 10 | 12 | y |
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| pwee | 256 | 1280 (5) | 4 | 16 (parallel) | y |
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| 24 |
| dpwee | 256 | 1280 (5) | 4 | 16 (parallel + differential) | y |
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| small | 384 | 1536 (4) | 6 | 12 | n |
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| little | 320 | 1792 (5.6) | 5 | 14 | y |
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| dlittle | 320 | 1792 (5.6) | 5 | 14 (differential) | y |
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| medium | 512 | 2048 (4) | 8 | 12 | y |
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| mediumd | 512 | 2048 (4) | 8 | 20 | y |
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| betwixt | 640 | 2560 (4) | 10 | 12 | y |
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