DeiT-base-DatasetDict({
train: Dataset({
features: ['img', 'fine_label', 'coarse_label'],
num_rows: 50000
})
test: Dataset({
features: ['img', 'fine_label', 'coarse_label'],
num_rows: 10000
})
validation: Dataset({
features: ['img', 'fine_label', 'coarse_label'],
num_rows: 10000
})
})
This model is a fine-tuned version of facebook/deit-base-distilled-patch16-224 on the cifar100 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3054
- Accuracy: 0.906
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: 64
- eval_batch_size: 1
- seed: 777
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 1.1232 | 1.0 | 782 | 0.8416 | 0.5390 |
| 0.9017 | 2.0 | 1564 | 0.8699 | 0.4365 |
| 0.7565 | 3.0 | 2346 | 0.8858 | 0.3678 |
| 0.706 | 4.0 | 3128 | 0.8952 | 0.3446 |
| 0.6353 | 5.0 | 3910 | 0.8986 | 0.3331 |
| 0.5384 | 6.0 | 4692 | 0.9001 | 0.3223 |
| 0.5004 | 7.0 | 5474 | 0.9018 | 0.3249 |
| 0.4672 | 8.0 | 6256 | 0.904 | 0.3113 |
| 0.4526 | 9.0 | 7038 | 0.9054 | 0.3081 |
| 0.4289 | 10.0 | 7820 | 0.906 | 0.3054 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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