Image Classification
Transformers
PyTorch
TensorBoard
English
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use DunnBC22/vit-base-patch16-224-in21k_Simpsons_Family_Members 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_Simpsons_Family_Members 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_Simpsons_Family_Members") 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_Simpsons_Family_Members") model = AutoModelForImageClassification.from_pretrained("DunnBC22/vit-base-patch16-224-in21k_Simpsons_Family_Members") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dcefbb16d932d5b4d5bfbb500b744d5d20d1c3a09942ed5fb3cad8b29398a2ce
- Size of remote file:
- 343 MB
- SHA256:
- e7288a5361276d32514e46cc052f0df086a40a9aaa2bdd0573c24bdadadb2a19
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.