Instructions to use timm/vit_dwee_patch16_reg1_gap_256.sbb_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_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/vit_dwee_patch16_reg1_gap_256.sbb_in1k", pretrained=True) - Transformers
How to use timm/vit_dwee_patch16_reg1_gap_256.sbb_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_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_dwee_patch16_reg1_gap_256.sbb_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update model config and README
Browse files
README.md
CHANGED
|
@@ -163,7 +163,7 @@ output = model.forward_head(output, pre_logits=True)
|
|
| 163 |
| [vit_betwixt_patch16_reg1_gap_256.sbb_in1k](https://huggingface.co/timm/vit_betwixt_patch16_reg1_gap_256.sbb_in1k) | 83.628 | 96.544 | 60.4 | 256 |
|
| 164 |
| [vit_medium_patch16_reg4_gap_256.sbb_in1k](https://huggingface.co/timm/vit_medium_patch16_reg4_gap_256.sbb_in1k) | 83.47 | 96.622 | 38.88 | 256 |
|
| 165 |
| [vit_medium_patch16_reg1_gap_256.sbb_in1k](https://huggingface.co/timm/vit_medium_patch16_reg1_gap_256.sbb_in1k) | 83.462 | 96.548 | 38.88 | 256 |
|
| 166 |
-
| [vit_dlittle_patch16_reg1_gap_256.
|
| 167 |
| [vit_little_patch16_reg4_gap_256.sbb_in1k](https://huggingface.co/timm/vit_little_patch16_reg4_gap_256.sbb_in1k) | 82.514 | 96.262 | 22.52 | 256 |
|
| 168 |
| [vit_dwee_patch16_reg1_gap_256.sbb_nadamuon_in1k](https://huggingface.co/timm/vit_dwee_patch16_reg1_gap_256.sbb_nadamuon_in1k) | 81.808 | 95.892 | 13.43 | 256 |
|
| 169 |
| [vit_dpwee_patch16_reg1_gap_256.sbb_nadamuon_in1k](https://huggingface.co/timm/vit_dpwee_patch16_reg1_gap_256.sbb_nadamuon_in1k) | 81.660 | 95.808 | 15.25 | 256 |
|
|
|
|
| 163 |
| [vit_betwixt_patch16_reg1_gap_256.sbb_in1k](https://huggingface.co/timm/vit_betwixt_patch16_reg1_gap_256.sbb_in1k) | 83.628 | 96.544 | 60.4 | 256 |
|
| 164 |
| [vit_medium_patch16_reg4_gap_256.sbb_in1k](https://huggingface.co/timm/vit_medium_patch16_reg4_gap_256.sbb_in1k) | 83.47 | 96.622 | 38.88 | 256 |
|
| 165 |
| [vit_medium_patch16_reg1_gap_256.sbb_in1k](https://huggingface.co/timm/vit_medium_patch16_reg1_gap_256.sbb_in1k) | 83.462 | 96.548 | 38.88 | 256 |
|
| 166 |
+
| [vit_dlittle_patch16_reg1_gap_256.sbb_nadamuon_in1k](https://huggingface.co/timm/vit_dlittle_patch16_reg1_gap_256.sbb_nadamuon_in1k) | 83.212 | 96.358 | 22.52 | 256 |
|
| 167 |
| [vit_little_patch16_reg4_gap_256.sbb_in1k](https://huggingface.co/timm/vit_little_patch16_reg4_gap_256.sbb_in1k) | 82.514 | 96.262 | 22.52 | 256 |
|
| 168 |
| [vit_dwee_patch16_reg1_gap_256.sbb_nadamuon_in1k](https://huggingface.co/timm/vit_dwee_patch16_reg1_gap_256.sbb_nadamuon_in1k) | 81.808 | 95.892 | 13.43 | 256 |
|
| 169 |
| [vit_dpwee_patch16_reg1_gap_256.sbb_nadamuon_in1k](https://huggingface.co/timm/vit_dpwee_patch16_reg1_gap_256.sbb_nadamuon_in1k) | 81.660 | 95.808 | 15.25 | 256 |
|