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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-base-22k-384 |
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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-randaug |
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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: 0.9470238095238095 |
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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-randaug |
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This model is a fine-tuned version of [facebook/convnextv2-base-22k-384](https://huggingface.co/facebook/convnextv2-base-22k-384) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2287 |
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- Accuracy: 0.9470 |
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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: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: cosine |
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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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| 0.6247 | 1.0 | 1099 | 0.3584 | 0.8982 | |
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| 0.4589 | 2.0 | 2198 | 0.2780 | 0.9229 | |
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| 0.3647 | 3.0 | 3297 | 0.2550 | 0.9264 | |
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| 0.3042 | 4.0 | 4396 | 0.2381 | 0.9400 | |
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| 0.2912 | 5.0 | 5495 | 0.2347 | 0.9419 | |
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| 0.2464 | 6.0 | 6594 | 0.2269 | 0.9459 | |
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| 0.2132 | 7.0 | 7693 | 0.2258 | 0.9483 | |
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| 0.1956 | 8.0 | 8792 | 0.2222 | 0.9495 | |
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| 0.1723 | 9.0 | 9891 | 0.2223 | 0.9499 | |
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| 0.1558 | 10.0 | 10990 | 0.2220 | 0.9507 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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