| --- |
| license: apache-2.0 |
| tags: |
| - image-classification |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: exper_batch_32_e4 |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # exper_batch_32_e4 |
| |
| 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_mesuem1 dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3909 |
| - Accuracy: 0.9067 |
|
|
| ## Model description |
|
|
| More information needed |
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|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
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|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0002 |
| - train_batch_size: 32 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 4 |
| - mixed_precision_training: Apex, opt level O1 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 3.4295 | 0.31 | 100 | 3.4027 | 0.2837 | |
| | 2.5035 | 0.62 | 200 | 2.4339 | 0.5247 | |
| | 1.6542 | 0.94 | 300 | 1.7690 | 0.6388 | |
| | 1.1589 | 1.25 | 400 | 1.3106 | 0.7460 | |
| | 0.9363 | 1.56 | 500 | 0.9977 | 0.7803 | |
| | 0.6946 | 1.88 | 600 | 0.8138 | 0.8207 | |
| | 0.3488 | 2.19 | 700 | 0.6593 | 0.8489 | |
| | 0.2935 | 2.5 | 800 | 0.5725 | 0.8662 | |
| | 0.2557 | 2.81 | 900 | 0.5088 | 0.8855 | |
| | 0.1509 | 3.12 | 1000 | 0.4572 | 0.8971 | |
| | 0.1367 | 3.44 | 1100 | 0.4129 | 0.9090 | |
| | 0.1078 | 3.75 | 1200 | 0.3909 | 0.9067 | |
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| ### Framework versions |
|
|
| - Transformers 4.19.4 |
| - Pytorch 1.5.1 |
| - Datasets 2.3.2 |
| - Tokenizers 0.12.1 |
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|
