|
--- |
|
license: apache-2.0 |
|
tags: |
|
- image-classification |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: exper6_mesum5 |
|
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. --> |
|
|
|
# exper6_mesum5 |
|
|
|
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. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.8241 |
|
- Accuracy: 0.8036 |
|
|
|
## 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: 16 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 3.9276 | 0.23 | 100 | 3.8550 | 0.2089 | |
|
| 3.0853 | 0.47 | 200 | 3.1106 | 0.3414 | |
|
| 2.604 | 0.7 | 300 | 2.5732 | 0.4379 | |
|
| 2.3183 | 0.93 | 400 | 2.2308 | 0.4882 | |
|
| 1.5326 | 1.16 | 500 | 1.7903 | 0.5828 | |
|
| 1.3367 | 1.4 | 600 | 1.5524 | 0.6349 | |
|
| 1.1544 | 1.63 | 700 | 1.3167 | 0.6645 | |
|
| 1.0788 | 1.86 | 800 | 1.3423 | 0.6385 | |
|
| 0.6762 | 2.09 | 900 | 1.0780 | 0.7124 | |
|
| 0.6483 | 2.33 | 1000 | 1.0090 | 0.7284 | |
|
| 0.6321 | 2.56 | 1100 | 1.0861 | 0.7024 | |
|
| 0.5558 | 2.79 | 1200 | 0.9933 | 0.7183 | |
|
| 0.342 | 3.02 | 1300 | 0.8871 | 0.7462 | |
|
| 0.2964 | 3.26 | 1400 | 0.9330 | 0.7408 | |
|
| 0.1959 | 3.49 | 1500 | 0.9367 | 0.7343 | |
|
| 0.368 | 3.72 | 1600 | 0.8472 | 0.7550 | |
|
| 0.1821 | 3.95 | 1700 | 0.8937 | 0.7568 | |
|
| 0.1851 | 4.19 | 1800 | 0.9546 | 0.7485 | |
|
| 0.1648 | 4.42 | 1900 | 0.9790 | 0.7355 | |
|
| 0.172 | 4.65 | 2000 | 0.8947 | 0.7627 | |
|
| 0.0928 | 4.88 | 2100 | 1.0093 | 0.7462 | |
|
| 0.0699 | 5.12 | 2200 | 0.8374 | 0.7639 | |
|
| 0.0988 | 5.35 | 2300 | 0.9189 | 0.7645 | |
|
| 0.0822 | 5.58 | 2400 | 0.9512 | 0.7580 | |
|
| 0.1223 | 5.81 | 2500 | 1.0809 | 0.7349 | |
|
| 0.0509 | 6.05 | 2600 | 0.9297 | 0.7769 | |
|
| 0.0511 | 6.28 | 2700 | 0.8981 | 0.7822 | |
|
| 0.0596 | 6.51 | 2800 | 0.9468 | 0.7704 | |
|
| 0.0494 | 6.74 | 2900 | 0.9045 | 0.7870 | |
|
| 0.0643 | 6.98 | 3000 | 1.1559 | 0.7391 | |
|
| 0.0158 | 7.21 | 3100 | 0.8450 | 0.7899 | |
|
| 0.0129 | 7.44 | 3200 | 0.8241 | 0.8036 | |
|
| 0.0441 | 7.67 | 3300 | 0.9679 | 0.7751 | |
|
| 0.0697 | 7.91 | 3400 | 1.0387 | 0.7751 | |
|
| 0.0084 | 8.14 | 3500 | 0.9441 | 0.7947 | |
|
| 0.0182 | 8.37 | 3600 | 0.8967 | 0.7994 | |
|
| 0.0042 | 8.6 | 3700 | 0.8750 | 0.8041 | |
|
| 0.0028 | 8.84 | 3800 | 0.9349 | 0.8041 | |
|
| 0.0053 | 9.07 | 3900 | 0.9403 | 0.7982 | |
|
| 0.0266 | 9.3 | 4000 | 0.9966 | 0.7959 | |
|
| 0.0022 | 9.53 | 4100 | 0.9472 | 0.8018 | |
|
| 0.0018 | 9.77 | 4200 | 0.8717 | 0.8136 | |
|
| 0.0018 | 10.0 | 4300 | 0.8964 | 0.8083 | |
|
| 0.0046 | 10.23 | 4400 | 0.8623 | 0.8160 | |
|
| 0.0037 | 10.47 | 4500 | 0.8762 | 0.8172 | |
|
| 0.0013 | 10.7 | 4600 | 0.9028 | 0.8142 | |
|
| 0.0013 | 10.93 | 4700 | 0.9084 | 0.8178 | |
|
| 0.0013 | 11.16 | 4800 | 0.8733 | 0.8213 | |
|
| 0.001 | 11.4 | 4900 | 0.8823 | 0.8207 | |
|
| 0.0009 | 11.63 | 5000 | 0.8769 | 0.8213 | |
|
| 0.0282 | 11.86 | 5100 | 0.8791 | 0.8219 | |
|
| 0.001 | 12.09 | 5200 | 0.8673 | 0.8249 | |
|
| 0.0016 | 12.33 | 5300 | 0.8633 | 0.8225 | |
|
| 0.0008 | 12.56 | 5400 | 0.8766 | 0.8195 | |
|
| 0.0008 | 12.79 | 5500 | 0.8743 | 0.8225 | |
|
| 0.0008 | 13.02 | 5600 | 0.8752 | 0.8231 | |
|
| 0.0008 | 13.26 | 5700 | 0.8676 | 0.8237 | |
|
| 0.0007 | 13.49 | 5800 | 0.8677 | 0.8237 | |
|
| 0.0008 | 13.72 | 5900 | 0.8703 | 0.8237 | |
|
| 0.0007 | 13.95 | 6000 | 0.8725 | 0.8237 | |
|
| 0.0006 | 14.19 | 6100 | 0.8741 | 0.8231 | |
|
| 0.0006 | 14.42 | 6200 | 0.8758 | 0.8237 | |
|
| 0.0008 | 14.65 | 6300 | 0.8746 | 0.8243 | |
|
| 0.0007 | 14.88 | 6400 | 0.8759 | 0.8243 | |
|
| 0.0007 | 15.12 | 6500 | 0.8803 | 0.8231 | |
|
| 0.0007 | 15.35 | 6600 | 0.8808 | 0.8237 | |
|
| 0.0007 | 15.58 | 6700 | 0.8798 | 0.8243 | |
|
| 0.0007 | 15.81 | 6800 | 0.8805 | 0.8243 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.20.1 |
|
- Pytorch 1.12.0+cu113 |
|
- Datasets 2.3.2 |
|
- Tokenizers 0.12.1 |
|
|
