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license: apache-2.0 |
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tags: |
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- image-classification |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: exper4_mesum5 |
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results: [] |
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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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# exper4_mesum5 |
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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 sudo-s/herbier_mesuem5 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4389 |
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- Accuracy: 0.1331 |
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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: 2e-05 |
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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: 4 |
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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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| 3.3793 | 0.23 | 100 | 3.4527 | 0.1308 | |
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| 3.2492 | 0.47 | 200 | 3.4501 | 0.1331 | |
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| 3.3847 | 0.7 | 300 | 3.4500 | 0.1272 | |
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| 3.3739 | 0.93 | 400 | 3.4504 | 0.1320 | |
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| 3.4181 | 1.16 | 500 | 3.4452 | 0.1320 | |
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| 3.214 | 1.4 | 600 | 3.4503 | 0.1320 | |
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| 3.282 | 1.63 | 700 | 3.4444 | 0.1325 | |
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| 3.5308 | 1.86 | 800 | 3.4473 | 0.1337 | |
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| 3.2251 | 2.09 | 900 | 3.4415 | 0.1361 | |
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| 3.4385 | 2.33 | 1000 | 3.4408 | 0.1343 | |
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| 3.3702 | 2.56 | 1100 | 3.4406 | 0.1325 | |
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| 3.366 | 2.79 | 1200 | 3.4411 | 0.1355 | |
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| 3.2022 | 3.02 | 1300 | 3.4403 | 0.1308 | |
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| 3.2768 | 3.26 | 1400 | 3.4394 | 0.1320 | |
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| 3.3444 | 3.49 | 1500 | 3.4394 | 0.1314 | |
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| 3.2981 | 3.72 | 1600 | 3.4391 | 0.1331 | |
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| 3.3349 | 3.95 | 1700 | 3.4389 | 0.1331 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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