|
--- |
|
license: apache-2.0 |
|
base_model: google/vit-large-patch16-224 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: 0.50-Train-Test-vit-large |
|
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. --> |
|
|
|
# 0.50-Train-Test-vit-large |
|
|
|
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.8804 |
|
- Accuracy: 0.8098 |
|
|
|
## 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: 5e-05 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 64 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- num_epochs: 25 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-------:|:----:|:---------------:|:--------:| |
|
| 2.3722 | 0.9825 | 14 | 1.8140 | 0.3758 | |
|
| 1.7117 | 1.9649 | 28 | 0.9446 | 0.7383 | |
|
| 0.3741 | 2.9474 | 42 | 0.8083 | 0.7338 | |
|
| 0.1709 | 4.0 | 57 | 0.7460 | 0.7562 | |
|
| 0.0166 | 4.9825 | 71 | 0.7632 | 0.7763 | |
|
| 0.0087 | 5.9649 | 85 | 0.9165 | 0.7629 | |
|
| 0.013 | 6.9474 | 99 | 0.8161 | 0.7942 | |
|
| 0.0029 | 8.0 | 114 | 0.8216 | 0.7964 | |
|
| 0.0016 | 8.9825 | 128 | 0.8461 | 0.7919 | |
|
| 0.0009 | 9.9649 | 142 | 0.8528 | 0.7919 | |
|
| 0.0007 | 10.9474 | 156 | 0.8539 | 0.8031 | |
|
| 0.0006 | 12.0 | 171 | 0.8586 | 0.8054 | |
|
| 0.0006 | 12.9825 | 185 | 0.8622 | 0.8076 | |
|
| 0.0005 | 13.9649 | 199 | 0.8649 | 0.8098 | |
|
| 0.0005 | 14.9474 | 213 | 0.8677 | 0.8098 | |
|
| 0.0005 | 16.0 | 228 | 0.8706 | 0.8098 | |
|
| 0.0004 | 16.9825 | 242 | 0.8729 | 0.8098 | |
|
| 0.0004 | 17.9649 | 256 | 0.8747 | 0.8098 | |
|
| 0.0004 | 18.9474 | 270 | 0.8764 | 0.8076 | |
|
| 0.0004 | 20.0 | 285 | 0.8776 | 0.8098 | |
|
| 0.0004 | 20.9825 | 299 | 0.8789 | 0.8076 | |
|
| 0.0003 | 21.9649 | 313 | 0.8794 | 0.8098 | |
|
| 0.0003 | 22.9474 | 327 | 0.8801 | 0.8098 | |
|
| 0.0003 | 24.0 | 342 | 0.8804 | 0.8098 | |
|
| 0.0003 | 24.5614 | 350 | 0.8804 | 0.8098 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.2 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.19.2 |
|
- Tokenizers 0.19.1 |
|
|
