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
- Xet hash:
- dbe05676af564e8a0f1b2c0c4169f1b72a886f5fd7b9d8a3e0d9a45d01f7a378
- Size of remote file:
- 169 MB
- SHA256:
- 6548af09a8b0869b3bf62653e636169d58c2f55706f0cde486d75545fbb4655e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.