| --- |
| license: apache-2.0 |
| tags: |
| - image-classification |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: exper_batch_16_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_16_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: 0.3598 |
| - Accuracy: 0.9059 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0002 |
| - train_batch_size: 16 |
| - 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 3.7606 | 0.16 | 100 | 3.7839 | 0.1989 | |
| | 3.1072 | 0.31 | 200 | 3.0251 | 0.3285 | |
| | 2.4068 | 0.47 | 300 | 2.4380 | 0.4719 | |
| | 2.0881 | 0.63 | 400 | 2.0489 | 0.5412 | |
| | 1.6817 | 0.78 | 500 | 1.7968 | 0.6025 | |
| | 1.342 | 0.94 | 600 | 1.5044 | 0.6249 | |
| | 0.9343 | 1.1 | 700 | 1.1881 | 0.7132 | |
| | 0.9552 | 1.25 | 800 | 1.1064 | 0.7224 | |
| | 0.7265 | 1.41 | 900 | 0.9189 | 0.7768 | |
| | 0.6732 | 1.56 | 1000 | 0.9227 | 0.7606 | |
| | 0.5587 | 1.72 | 1100 | 0.7912 | 0.7903 | |
| | 0.6332 | 1.88 | 1200 | 0.7606 | 0.7945 | |
| | 0.3188 | 2.03 | 1300 | 0.6535 | 0.8288 | |
| | 0.3079 | 2.19 | 1400 | 0.5686 | 0.8577 | |
| | 0.2518 | 2.35 | 1500 | 0.5517 | 0.8577 | |
| | 0.2 | 2.5 | 1600 | 0.5277 | 0.8631 | |
| | 0.2032 | 2.66 | 1700 | 0.4841 | 0.8701 | |
| | 0.1555 | 2.82 | 1800 | 0.4578 | 0.8793 | |
| | 0.145 | 2.97 | 1900 | 0.4466 | 0.8755 | |
| | 0.0985 | 3.13 | 2000 | 0.4249 | 0.8867 | |
| | 0.0955 | 3.29 | 2100 | 0.3977 | 0.8932 | |
| | 0.0438 | 3.44 | 2200 | 0.3785 | 0.9036 | |
| | 0.0589 | 3.6 | 2300 | 0.3717 | 0.9017 | |
| | 0.0709 | 3.76 | 2400 | 0.3609 | 0.9052 | |
| | 0.0706 | 3.91 | 2500 | 0.3598 | 0.9059 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.19.4 |
| - Pytorch 1.5.1 |
| - Datasets 2.3.2 |
| - Tokenizers 0.12.1 |
|
|
