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:
- fbc99a008076b93097406afc4d50f28295e2aa208a1498d1762f66eef9f827a6
- Size of remote file:
- 6.15 kB
- SHA256:
- 0597414ccfa8b0c3db38b4d1aa8207f3095e97b9622a6a222a4baaff4584ee63
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.