Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use decipherme/bhutanese_currency_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use decipherme/bhutanese_currency_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="decipherme/bhutanese_currency_model") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("decipherme/bhutanese_currency_model") model = AutoModelForImageClassification.from_pretrained("decipherme/bhutanese_currency_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bhutanese_currency_model
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3152
- Accuracy: 0.975
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.9781 | 1.0 | 20 | 1.8946 | 0.5844 |
| 1.5451 | 2.0 | 40 | 1.4346 | 0.8781 |
| 1.0404 | 3.0 | 60 | 0.9740 | 0.9281 |
| 0.7007 | 4.0 | 80 | 0.6779 | 0.9656 |
| 0.5091 | 5.0 | 100 | 0.4903 | 0.9781 |
| 0.3778 | 6.0 | 120 | 0.4151 | 0.9656 |
| 0.3274 | 7.0 | 140 | 0.3602 | 0.9812 |
| 0.2779 | 8.0 | 160 | 0.3155 | 0.9875 |
| 0.2644 | 9.0 | 180 | 0.2932 | 0.9812 |
| 0.2576 | 10.0 | 200 | 0.3152 | 0.975 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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Model tree for decipherme/bhutanese_currency_model
Base model
google/vit-base-patch16-224-in21kEvaluation results
- Accuracy on imagefolderself-reported0.975