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beit-base-patch16-224-pt22k-ft22k-finetuned-eurosat
This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.5698
- Accuracy: 0.7826
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 | 0.8366 | 0.4348 |
| No log | 1.57 | 2 | 0.7708 | 0.5217 |
| No log | 2.57 | 3 | 0.7185 | 0.6522 |
| No log | 3.57 | 4 | 0.6747 | 0.6522 |
| No log | 4.57 | 5 | 0.6380 | 0.6522 |
| No log | 5.57 | 6 | 0.6098 | 0.6957 |
| No log | 6.57 | 7 | 0.5859 | 0.7391 |
| No log | 7.57 | 8 | 0.5698 | 0.7826 |
| No log | 8.57 | 9 | 0.5589 | 0.7826 |
| 1.0859 | 9.57 | 10 | 0.5534 | 0.7826 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
