Image Classification
Transformers
PyTorch
TensorBoard
English
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use DunnBC22/vit-base-patch16-224-in21k-Brain_Tumors_Image_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/vit-base-patch16-224-in21k-Brain_Tumors_Image_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DunnBC22/vit-base-patch16-224-in21k-Brain_Tumors_Image_Classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("DunnBC22/vit-base-patch16-224-in21k-Brain_Tumors_Image_Classification") model = AutoModelForImageClassification.from_pretrained("DunnBC22/vit-base-patch16-224-in21k-Brain_Tumors_Image_Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cc38d53aa9352f0f417d3a88c8a06be4767c6f7da845fea45c98b1e188f088ce
- Size of remote file:
- 3.71 kB
- SHA256:
- 0df4ef1499a4c3d3b3d262ae417f42f1ab6f969e853fd1b07eb05df2daa132b9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.