my-indian-food-model-v25-full
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: 0.4365
- Accuracy: 0.9793
- F1: 0.9793
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.773 | 1.0 | 254 | 0.5546 | 0.9697 | 0.9695 |
| 0.3264 | 2.0 | 508 | 0.3322 | 0.9744 | 0.9744 |
| 0.1531 | 3.0 | 762 | 0.2613 | 0.9773 | 0.9774 |
| 0.0475 | 4.0 | 1016 | 0.2606 | 0.9805 | 0.9806 |
| 0.0077 | 5.0 | 1270 | 0.2874 | 0.9764 | 0.9763 |
| 0.0055 | 6.0 | 1524 | 0.3696 | 0.9776 | 0.9776 |
| 0.0106 | 7.0 | 1778 | 0.4365 | 0.9793 | 0.9793 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.6.0+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1
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Evaluation results
- Accuracy on imagefolderself-reported0.979
- F1 on imagefolderself-reported0.979