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---
license: apache-2.0
base_model: google/vit-base-patch16-384
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: rmsProp_vitB-32-384-2e4-ne-1-bs-16
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: 0.9827586206896551
---
<!-- 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. -->
# rmsProp_vitB-32-384-2e4-ne-1-bs-16
This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0752
- Accuracy: 0.9828
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.774 | 0.07 | 100 | 3.4867 | 0.0819 |
| 1.1104 | 0.14 | 200 | 1.5104 | 0.5359 |
| 0.2994 | 0.21 | 300 | 0.3914 | 0.8836 |
| 0.2754 | 0.28 | 400 | 0.2456 | 0.9210 |
| 0.212 | 0.35 | 500 | 0.1669 | 0.9511 |
| 0.1063 | 0.42 | 600 | 0.1406 | 0.9511 |
| 0.0999 | 0.49 | 700 | 0.2218 | 0.9425 |
| 0.0609 | 0.56 | 800 | 0.1407 | 0.9598 |
| 0.0784 | 0.63 | 900 | 0.0985 | 0.9641 |
| 0.0251 | 0.7 | 1000 | 0.0963 | 0.9698 |
| 0.0094 | 0.77 | 1100 | 0.0893 | 0.9727 |
| 0.0153 | 0.84 | 1200 | 0.1044 | 0.9670 |
| 0.032 | 0.91 | 1300 | 0.1035 | 0.9713 |
| 0.0042 | 0.97 | 1400 | 0.0752 | 0.9828 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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