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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- image_folder |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-base-patch16-224-finetuned-eurosat |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: image_folder |
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type: image_folder |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9071691176470589 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# vit-base-patch16-224-finetuned-eurosat |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the image_folder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3209 |
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- Accuracy: 0.9072 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.5417 | 0.99 | 76 | 0.5556 | 0.8263 | |
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| 0.4853 | 1.99 | 152 | 0.5319 | 0.8199 | |
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| 0.4926 | 2.99 | 228 | 0.5133 | 0.8539 | |
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| 0.4131 | 3.99 | 304 | 0.4481 | 0.8603 | |
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| 0.4081 | 4.99 | 380 | 0.4280 | 0.8824 | |
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| 0.3287 | 5.99 | 456 | 0.4330 | 0.8667 | |
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| 0.3381 | 6.99 | 532 | 0.3549 | 0.8888 | |
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| 0.3182 | 7.99 | 608 | 0.3382 | 0.8961 | |
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| 0.3046 | 8.99 | 684 | 0.3790 | 0.8925 | |
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| 0.3093 | 9.99 | 760 | 0.3209 | 0.9072 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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