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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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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: beit-base-patch16-224-pt22k-ft22k-finetunedt |
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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: train |
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split: train |
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args: train |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 1.0 |
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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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# beit-base-patch16-224-pt22k-ft22k-finetunedt |
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This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0000 |
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- Accuracy: 1.0 |
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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: 15 |
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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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| 0.7369 | 1.0 | 25 | 0.0425 | 0.9972 | |
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| 0.007 | 2.0 | 50 | 0.0005 | 1.0 | |
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| 0.0041 | 3.0 | 75 | 0.0003 | 1.0 | |
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| 0.0011 | 4.0 | 100 | 0.0002 | 1.0 | |
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| 0.0008 | 5.0 | 125 | 0.0001 | 1.0 | |
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| 0.0055 | 6.0 | 150 | 0.0002 | 1.0 | |
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| 0.0007 | 7.0 | 175 | 0.0001 | 1.0 | |
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| 0.0047 | 8.0 | 200 | 0.0001 | 1.0 | |
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| 0.0005 | 9.0 | 225 | 0.0001 | 1.0 | |
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| 0.006 | 10.0 | 250 | 0.0001 | 1.0 | |
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| 0.0065 | 11.0 | 275 | 0.0001 | 1.0 | |
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| 0.0023 | 12.0 | 300 | 0.0001 | 1.0 | |
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| 0.0003 | 13.0 | 325 | 0.0001 | 1.0 | |
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| 0.0011 | 14.0 | 350 | 0.0000 | 1.0 | |
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| 0.0003 | 15.0 | 375 | 0.0000 | 1.0 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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