<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
vit-base-patch16-224-finetuned-eurosat
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.5186
- Accuracy: 0.7391
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: 1e-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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.57 | 1 | 0.6360 | 0.6522 |
| No log | 1.57 | 2 | 0.6056 | 0.6957 |
| No log | 2.57 | 3 | 0.5824 | 0.6957 |
| No log | 3.57 | 4 | 0.5638 | 0.6957 |
| No log | 4.57 | 5 | 0.5497 | 0.6957 |
| No log | 5.57 | 6 | 0.5388 | 0.6522 |
| No log | 6.57 | 7 | 0.5296 | 0.6522 |
| No log | 7.57 | 8 | 0.5232 | 0.6522 |
| No log | 8.57 | 9 | 0.5186 | 0.7391 |
| 1.0722 | 9.57 | 10 | 0.5163 | 0.7391 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
