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
| { | |
| "epoch": 2.0, | |
| "eval_Macro F1": 1.0, | |
| "eval_Macro Precision": 1.0, | |
| "eval_Macro Recall": 1.0, | |
| "eval_Micro F1": 1.0, | |
| "eval_Micro Precision": 1.0, | |
| "eval_Micro Recall": 1.0, | |
| "eval_Weighted F1": 1.0, | |
| "eval_Weighted Precision": 1.0, | |
| "eval_Weighted Recall": 1.0, | |
| "eval_accuracy": 1.0, | |
| "eval_loss": 0.018915435299277306, | |
| "eval_runtime": 387.564, | |
| "eval_samples_per_second": 2.064, | |
| "eval_steps_per_second": 0.258, | |
| "train_loss": 0.13119665324687957, | |
| "train_runtime": 9143.9921, | |
| "train_samples_per_second": 0.7, | |
| "train_steps_per_second": 0.044 | |
| } |