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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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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: xyz |
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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: test |
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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: 0.565 |
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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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# xyz |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0792 |
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- Accuracy: 0.565 |
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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: 0.0002 |
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- train_batch_size: 48 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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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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| 0.8643 | 0.55 | 100 | 1.7445 | 0.45 | |
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| 0.3609 | 1.09 | 200 | 1.4977 | 0.565 | |
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| 0.2602 | 1.64 | 300 | 1.8113 | 0.525 | |
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| 0.1278 | 2.19 | 400 | 1.8174 | 0.53 | |
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| 0.051 | 2.73 | 500 | 1.9151 | 0.525 | |
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| 0.0619 | 3.28 | 600 | 2.0656 | 0.55 | |
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| 0.0263 | 3.83 | 700 | 2.1127 | 0.555 | |
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| 0.0104 | 4.37 | 800 | 2.1411 | 0.55 | |
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| 0.01 | 4.92 | 900 | 2.0792 | 0.565 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.0 |
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- Tokenizers 0.15.0 |
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