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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-384 |
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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: rmsProp_vitB-32-384-2e4-ne-1-bs-16 |
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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: train |
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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: 0.9827586206896551 |
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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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# rmsProp_vitB-32-384-2e4-ne-1-bs-16 |
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This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0752 |
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- Accuracy: 0.9828 |
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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: 16 |
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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: 1 |
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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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| 2.774 | 0.07 | 100 | 3.4867 | 0.0819 | |
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| 1.1104 | 0.14 | 200 | 1.5104 | 0.5359 | |
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| 0.2994 | 0.21 | 300 | 0.3914 | 0.8836 | |
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| 0.2754 | 0.28 | 400 | 0.2456 | 0.9210 | |
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| 0.212 | 0.35 | 500 | 0.1669 | 0.9511 | |
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| 0.1063 | 0.42 | 600 | 0.1406 | 0.9511 | |
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| 0.0999 | 0.49 | 700 | 0.2218 | 0.9425 | |
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| 0.0609 | 0.56 | 800 | 0.1407 | 0.9598 | |
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| 0.0784 | 0.63 | 900 | 0.0985 | 0.9641 | |
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| 0.0251 | 0.7 | 1000 | 0.0963 | 0.9698 | |
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| 0.0094 | 0.77 | 1100 | 0.0893 | 0.9727 | |
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| 0.0153 | 0.84 | 1200 | 0.1044 | 0.9670 | |
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| 0.032 | 0.91 | 1300 | 0.1035 | 0.9713 | |
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| 0.0042 | 0.97 | 1400 | 0.0752 | 0.9828 | |
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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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