Image Classification
Transformers
PyTorch
TensorBoard
English
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use DunnBC22/vit-base-patch16-224-in21k-Mango_leaf_Disease 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-Mango_leaf_Disease 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-Mango_leaf_Disease") 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-Mango_leaf_Disease") model = AutoModelForImageClassification.from_pretrained("DunnBC22/vit-base-patch16-224-in21k-Mango_leaf_Disease") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2e7107be2f0c5656b84683255e40b5119ee535e8736c6b278c720d47e835f81
- Size of remote file:
- 343 MB
- SHA256:
- aee9af8f21b0da9318b8d92707fe22c15aa9523a9a611df48ec9360e82e5f386
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