Image Classification
timm
PyTorch
Safetensors
Transformers
variance_pad_eva
feature-extraction
custom_code
Instructions to use bn22/vit_medium_patch28_rope_reg4_gap_224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use bn22/vit_medium_patch28_rope_reg4_gap_224 with timm:
import timm model = timm.create_model("hf_hub:bn22/vit_medium_patch28_rope_reg4_gap_224", pretrained=True) - Transformers
How to use bn22/vit_medium_patch28_rope_reg4_gap_224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bn22/vit_medium_patch28_rope_reg4_gap_224", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bn22/vit_medium_patch28_rope_reg4_gap_224", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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