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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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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-U8-10b |
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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: imagefolder |
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type: imagefolder |
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config: default |
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split: validation |
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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: 0.8627450980392157 |
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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-U8-10b |
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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 imagefolder dataset "dmae-ve-U8". |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5349 |
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- Accuracy: 0.8627 |
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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: 5.5e-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: 0.05 |
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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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| 1.2473 | 1.0 | 20 | 1.1671 | 0.5882 | |
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| 0.955 | 2.0 | 40 | 0.9392 | 0.6471 | |
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| 0.735 | 3.0 | 60 | 0.7247 | 0.6863 | |
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| 0.5341 | 4.0 | 80 | 0.5977 | 0.8235 | |
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| 0.3864 | 5.0 | 100 | 0.6556 | 0.7451 | |
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| 0.2837 | 6.0 | 120 | 0.6781 | 0.7255 | |
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| 0.2332 | 7.0 | 140 | 0.5419 | 0.8431 | |
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| 0.1974 | 8.0 | 160 | 0.5349 | 0.8627 | |
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| 0.1857 | 9.0 | 180 | 0.5606 | 0.8235 | |
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| 0.1907 | 10.0 | 200 | 0.4875 | 0.8431 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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