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---
license: apache-2.0
base_model: google/vit-large-patch32-384
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vitLarge-p32-384-2e-4-batch_16_epoch_4_classes_24
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: 0.978448275862069
---
<!-- 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. -->
# vitLarge-p32-384-2e-4-batch_16_epoch_4_classes_24
This model is a fine-tuned version of [google/vit-large-patch32-384](https://huggingface.co/google/vit-large-patch32-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0894
- Accuracy: 0.9784
## 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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3286 | 0.07 | 100 | 0.3636 | 0.8764 |
| 0.3196 | 0.14 | 200 | 0.4602 | 0.875 |
| 0.1705 | 0.21 | 300 | 0.2507 | 0.9152 |
| 0.2873 | 0.28 | 400 | 0.2614 | 0.9282 |
| 0.0982 | 0.35 | 500 | 0.2327 | 0.9310 |
| 0.1669 | 0.42 | 600 | 0.3202 | 0.9210 |
| 0.1612 | 0.49 | 700 | 0.5062 | 0.8807 |
| 0.1532 | 0.56 | 800 | 0.2240 | 0.9425 |
| 0.1728 | 0.63 | 900 | 0.1601 | 0.9511 |
| 0.167 | 0.7 | 1000 | 0.3861 | 0.9138 |
| 0.0752 | 0.77 | 1100 | 0.2198 | 0.9483 |
| 0.0423 | 0.84 | 1200 | 0.2114 | 0.9440 |
| 0.0898 | 0.91 | 1300 | 0.1613 | 0.9511 |
| 0.0498 | 0.97 | 1400 | 0.2824 | 0.9382 |
| 0.016 | 1.04 | 1500 | 0.1921 | 0.9569 |
| 0.0079 | 1.11 | 1600 | 0.2548 | 0.9382 |
| 0.008 | 1.18 | 1700 | 0.2186 | 0.9497 |
| 0.049 | 1.25 | 1800 | 0.2018 | 0.9569 |
| 0.0333 | 1.32 | 1900 | 0.1676 | 0.9598 |
| 0.1119 | 1.39 | 2000 | 0.1601 | 0.9583 |
| 0.0134 | 1.46 | 2100 | 0.1157 | 0.9741 |
| 0.0192 | 1.53 | 2200 | 0.1320 | 0.9641 |
| 0.0085 | 1.6 | 2300 | 0.1590 | 0.9641 |
| 0.0384 | 1.67 | 2400 | 0.0973 | 0.9741 |
| 0.0531 | 1.74 | 2500 | 0.1719 | 0.9569 |
| 0.0221 | 1.81 | 2600 | 0.1280 | 0.9741 |
| 0.0006 | 1.88 | 2700 | 0.1895 | 0.9540 |
| 0.0006 | 1.95 | 2800 | 0.1258 | 0.9713 |
| 0.016 | 2.02 | 2900 | 0.1105 | 0.9713 |
| 0.0004 | 2.09 | 3000 | 0.1118 | 0.9684 |
| 0.0001 | 2.16 | 3100 | 0.0936 | 0.9684 |
| 0.0003 | 2.23 | 3200 | 0.0932 | 0.9684 |
| 0.0001 | 2.3 | 3300 | 0.1247 | 0.9713 |
| 0.0004 | 2.37 | 3400 | 0.0897 | 0.9741 |
| 0.0001 | 2.44 | 3500 | 0.0853 | 0.9784 |
| 0.0002 | 2.51 | 3600 | 0.0948 | 0.9770 |
| 0.0002 | 2.58 | 3700 | 0.0957 | 0.9770 |
| 0.0043 | 2.65 | 3800 | 0.0868 | 0.9756 |
| 0.0001 | 2.72 | 3900 | 0.0904 | 0.9741 |
| 0.0011 | 2.79 | 4000 | 0.0881 | 0.9770 |
| 0.0001 | 2.86 | 4100 | 0.0890 | 0.9784 |
| 0.0001 | 2.92 | 4200 | 0.0896 | 0.9784 |
| 0.0001 | 2.99 | 4300 | 0.0894 | 0.9784 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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