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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: vit-base-patch16-224-Trial007-YEL_STEM2 |
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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: 0.9814814814814815 |
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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-Trial007-YEL_STEM2 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1172 |
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- Accuracy: 0.9815 |
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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: 60 |
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- eval_batch_size: 60 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 240 |
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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: 50 |
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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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| 0.6676 | 0.89 | 2 | 0.6180 | 0.7222 | |
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| 0.5805 | 1.78 | 4 | 0.5004 | 0.7593 | |
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| 0.5012 | 2.67 | 6 | 0.3783 | 0.9630 | |
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| 0.2794 | 4.0 | 9 | 0.2285 | 0.9630 | |
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| 0.2695 | 4.89 | 11 | 0.2551 | 0.8889 | |
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| 0.2782 | 5.78 | 13 | 0.1079 | 0.9630 | |
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| 0.2131 | 6.67 | 15 | 0.1205 | 0.9630 | |
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| 0.1537 | 8.0 | 18 | 0.1861 | 0.9630 | |
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| 0.1739 | 8.89 | 20 | 0.1172 | 0.9815 | |
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| 0.1059 | 9.78 | 22 | 0.1092 | 0.9815 | |
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| 0.146 | 10.67 | 24 | 0.1072 | 0.9815 | |
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| 0.088 | 12.0 | 27 | 0.1015 | 0.9815 | |
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| 0.1304 | 12.89 | 29 | 0.1151 | 0.9815 | |
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| 0.0924 | 13.78 | 31 | 0.1313 | 0.9815 | |
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| 0.091 | 14.67 | 33 | 0.1178 | 0.9815 | |
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| 0.0508 | 16.0 | 36 | 0.0971 | 0.9815 | |
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| 0.1004 | 16.89 | 38 | 0.1175 | 0.9815 | |
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| 0.1097 | 17.78 | 40 | 0.1423 | 0.9630 | |
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| 0.0758 | 18.67 | 42 | 0.1597 | 0.9630 | |
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| 0.0687 | 20.0 | 45 | 0.1205 | 0.9815 | |
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| 0.0513 | 20.89 | 47 | 0.1107 | 0.9815 | |
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| 0.0755 | 21.78 | 49 | 0.1150 | 0.9815 | |
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| 0.0897 | 22.67 | 51 | 0.1332 | 0.9630 | |
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| 0.0439 | 24.0 | 54 | 0.1263 | 0.9815 | |
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| 0.0607 | 24.89 | 56 | 0.1111 | 0.9815 | |
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| 0.0719 | 25.78 | 58 | 0.1004 | 0.9815 | |
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| 0.0599 | 26.67 | 60 | 0.1064 | 0.9815 | |
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| 0.0613 | 28.0 | 63 | 0.1355 | 0.9815 | |
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| 0.0689 | 28.89 | 65 | 0.1444 | 0.9815 | |
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| 0.0754 | 29.78 | 67 | 0.1398 | 0.9815 | |
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| 0.0835 | 30.67 | 69 | 0.1345 | 0.9815 | |
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| 0.0801 | 32.0 | 72 | 0.1348 | 0.9815 | |
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| 0.0701 | 32.89 | 74 | 0.1365 | 0.9815 | |
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| 0.0647 | 33.78 | 76 | 0.1348 | 0.9815 | |
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| 0.0982 | 34.67 | 78 | 0.1346 | 0.9815 | |
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| 0.0671 | 36.0 | 81 | 0.1378 | 0.9815 | |
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| 0.054 | 36.89 | 83 | 0.1371 | 0.9815 | |
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| 0.0735 | 37.78 | 85 | 0.1355 | 0.9815 | |
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| 0.0736 | 38.67 | 87 | 0.1349 | 0.9815 | |
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| 0.0287 | 40.0 | 90 | 0.1329 | 0.9815 | |
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| 0.0539 | 40.89 | 92 | 0.1322 | 0.9815 | |
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| 0.0483 | 41.78 | 94 | 0.1324 | 0.9815 | |
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| 0.083 | 42.67 | 96 | 0.1319 | 0.9815 | |
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| 0.0558 | 44.0 | 99 | 0.1319 | 0.9815 | |
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| 0.0752 | 44.44 | 100 | 0.1319 | 0.9815 | |
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### Framework versions |
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- Transformers 4.30.0.dev0 |
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- Pytorch 1.12.1 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.1 |
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