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vit-base-patch16-224-in21k-Dog-Classification
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagewoof dataset. It achieves the following results on the evaluation set:
- Loss: 0.5386
- Accuracy: 0.9524
Model description
Based on the frgfm/imagewoof dataset, it can categorize ten types of dogs such as Shih-Tzu, Rhodesian ridgeback, Beagle, English foxhound, Border terrier, Australian terrier, Golden retriever, Old English sheepdog, Samoyed, Dingo.
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.2356 | 0.99 | 63 | 1.0520 | 0.9059 |
| 0.6987 | 2.0 | 127 | 0.6162 | 0.9446 |
| 0.5787 | 2.98 | 189 | 0.5386 | 0.9524 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.13.0+cu117
- Datasets 2.14.0
- Tokenizers 0.13.3
