|
|
--- |
|
|
license: apache-2.0 |
|
|
tags: |
|
|
- image-classification |
|
|
- generated_from_trainer |
|
|
metrics: |
|
|
- accuracy |
|
|
model-index: |
|
|
- name: exper_batch_32_e8 |
|
|
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_e8 |
|
|
|
|
|
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: 0.3520 |
|
|
- Accuracy: 0.9113 |
|
|
|
|
|
## 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: 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: 8 |
|
|
- mixed_precision_training: Apex, opt level O1 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
|
| 3.3787 | 0.31 | 100 | 3.3100 | 0.3566 | |
|
|
| 2.3975 | 0.62 | 200 | 2.3196 | 0.5717 | |
|
|
| 1.5578 | 0.94 | 300 | 1.6764 | 0.6461 | |
|
|
| 1.0291 | 1.25 | 400 | 1.1713 | 0.7463 | |
|
|
| 0.8185 | 1.56 | 500 | 0.9292 | 0.7953 | |
|
|
| 0.6181 | 1.88 | 600 | 0.7732 | 0.8169 | |
|
|
| 0.3873 | 2.19 | 700 | 0.6877 | 0.8277 | |
|
|
| 0.2979 | 2.5 | 800 | 0.6250 | 0.8404 | |
|
|
| 0.2967 | 2.81 | 900 | 0.6151 | 0.8365 | |
|
|
| 0.1874 | 3.12 | 1000 | 0.5401 | 0.8608 | |
|
|
| 0.2232 | 3.44 | 1100 | 0.5032 | 0.8712 | |
|
|
| 0.1109 | 3.75 | 1200 | 0.4635 | 0.8774 | |
|
|
| 0.0539 | 4.06 | 1300 | 0.4495 | 0.8843 | |
|
|
| 0.0668 | 4.38 | 1400 | 0.4273 | 0.8951 | |
|
|
| 0.0567 | 4.69 | 1500 | 0.4427 | 0.8867 | |
|
|
| 0.0285 | 5.0 | 1600 | 0.4092 | 0.8955 | |
|
|
| 0.0473 | 5.31 | 1700 | 0.3720 | 0.9071 | |
|
|
| 0.0225 | 5.62 | 1800 | 0.3691 | 0.9063 | |
|
|
| 0.0196 | 5.94 | 1900 | 0.3775 | 0.9048 | |
|
|
| 0.0173 | 6.25 | 2000 | 0.3641 | 0.9040 | |
|
|
| 0.0092 | 6.56 | 2100 | 0.3551 | 0.9090 | |
|
|
| 0.008 | 6.88 | 2200 | 0.3591 | 0.9125 | |
|
|
| 0.0072 | 7.19 | 2300 | 0.3542 | 0.9121 | |
|
|
| 0.007 | 7.5 | 2400 | 0.3532 | 0.9106 | |
|
|
| 0.007 | 7.81 | 2500 | 0.3520 | 0.9113 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.19.4 |
|
|
- Pytorch 1.5.1 |
|
|
- Datasets 2.3.2 |
|
|
- Tokenizers 0.12.1 |
|
|
|
