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
File size: 609 Bytes
710350e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"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
} |