Instructions to use Docty/Mangovariety with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Docty/Mangovariety with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Docty/Mangovariety") 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("Docty/Mangovariety") model = AutoModelForImageClassification.from_pretrained("Docty/Mangovariety") - Notebooks
- Google Colab
- Kaggle
mango_output12
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the Docty/Mangovariety dataset. It achieves the following results on the evaluation set:
- Loss: 0.3909
- Accuracy: 0.9917
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 1337
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 85 | 1.2826 | 0.9375 |
| No log | 2.0 | 170 | 0.7519 | 0.975 |
| No log | 3.0 | 255 | 0.5236 | 0.9792 |
| No log | 4.0 | 340 | 0.4190 | 0.9875 |
| No log | 5.0 | 425 | 0.3909 | 0.9917 |
Framework versions
- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0
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Model tree for Docty/Mangovariety
Base model
google/vit-base-patch16-224-in21k