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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-Trial007-YEL_STEM2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9814814814814815
---
<!-- 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. -->
# vit-base-patch16-224-Trial007-YEL_STEM2
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1172
- Accuracy: 0.9815
## 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: 60
- eval_batch_size: 60
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 240
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6676 | 0.89 | 2 | 0.6180 | 0.7222 |
| 0.5805 | 1.78 | 4 | 0.5004 | 0.7593 |
| 0.5012 | 2.67 | 6 | 0.3783 | 0.9630 |
| 0.2794 | 4.0 | 9 | 0.2285 | 0.9630 |
| 0.2695 | 4.89 | 11 | 0.2551 | 0.8889 |
| 0.2782 | 5.78 | 13 | 0.1079 | 0.9630 |
| 0.2131 | 6.67 | 15 | 0.1205 | 0.9630 |
| 0.1537 | 8.0 | 18 | 0.1861 | 0.9630 |
| 0.1739 | 8.89 | 20 | 0.1172 | 0.9815 |
| 0.1059 | 9.78 | 22 | 0.1092 | 0.9815 |
| 0.146 | 10.67 | 24 | 0.1072 | 0.9815 |
| 0.088 | 12.0 | 27 | 0.1015 | 0.9815 |
| 0.1304 | 12.89 | 29 | 0.1151 | 0.9815 |
| 0.0924 | 13.78 | 31 | 0.1313 | 0.9815 |
| 0.091 | 14.67 | 33 | 0.1178 | 0.9815 |
| 0.0508 | 16.0 | 36 | 0.0971 | 0.9815 |
| 0.1004 | 16.89 | 38 | 0.1175 | 0.9815 |
| 0.1097 | 17.78 | 40 | 0.1423 | 0.9630 |
| 0.0758 | 18.67 | 42 | 0.1597 | 0.9630 |
| 0.0687 | 20.0 | 45 | 0.1205 | 0.9815 |
| 0.0513 | 20.89 | 47 | 0.1107 | 0.9815 |
| 0.0755 | 21.78 | 49 | 0.1150 | 0.9815 |
| 0.0897 | 22.67 | 51 | 0.1332 | 0.9630 |
| 0.0439 | 24.0 | 54 | 0.1263 | 0.9815 |
| 0.0607 | 24.89 | 56 | 0.1111 | 0.9815 |
| 0.0719 | 25.78 | 58 | 0.1004 | 0.9815 |
| 0.0599 | 26.67 | 60 | 0.1064 | 0.9815 |
| 0.0613 | 28.0 | 63 | 0.1355 | 0.9815 |
| 0.0689 | 28.89 | 65 | 0.1444 | 0.9815 |
| 0.0754 | 29.78 | 67 | 0.1398 | 0.9815 |
| 0.0835 | 30.67 | 69 | 0.1345 | 0.9815 |
| 0.0801 | 32.0 | 72 | 0.1348 | 0.9815 |
| 0.0701 | 32.89 | 74 | 0.1365 | 0.9815 |
| 0.0647 | 33.78 | 76 | 0.1348 | 0.9815 |
| 0.0982 | 34.67 | 78 | 0.1346 | 0.9815 |
| 0.0671 | 36.0 | 81 | 0.1378 | 0.9815 |
| 0.054 | 36.89 | 83 | 0.1371 | 0.9815 |
| 0.0735 | 37.78 | 85 | 0.1355 | 0.9815 |
| 0.0736 | 38.67 | 87 | 0.1349 | 0.9815 |
| 0.0287 | 40.0 | 90 | 0.1329 | 0.9815 |
| 0.0539 | 40.89 | 92 | 0.1322 | 0.9815 |
| 0.0483 | 41.78 | 94 | 0.1324 | 0.9815 |
| 0.083 | 42.67 | 96 | 0.1319 | 0.9815 |
| 0.0558 | 44.0 | 99 | 0.1319 | 0.9815 |
| 0.0752 | 44.44 | 100 | 0.1319 | 0.9815 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.1
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