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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-p16-384-2e-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: 0.985632183908046
---
<!-- 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-p16-384-2e-4-batch_16_epoch_4_classes_24
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: 0.0544
- Accuracy: 0.9856
## 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: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.0503 | 0.07 | 100 | 3.0273 | 0.0934 |
| 2.2522 | 0.14 | 200 | 2.4833 | 0.2328 |
| 1.5093 | 0.21 | 300 | 1.3361 | 0.5503 |
| 1.0645 | 0.28 | 400 | 1.0976 | 0.6580 |
| 0.5308 | 0.35 | 500 | 0.5680 | 0.8161 |
| 0.3545 | 0.42 | 600 | 0.3870 | 0.8664 |
| 0.2051 | 0.49 | 700 | 0.3348 | 0.9023 |
| 0.2241 | 0.56 | 800 | 0.1545 | 0.9411 |
| 0.2165 | 0.63 | 900 | 0.1722 | 0.9569 |
| 0.1589 | 0.7 | 1000 | 0.1554 | 0.9497 |
| 0.0647 | 0.77 | 1100 | 0.1400 | 0.9483 |
| 0.1178 | 0.84 | 1200 | 0.2000 | 0.9411 |
| 0.0518 | 0.91 | 1300 | 0.1856 | 0.9483 |
| 0.0433 | 0.97 | 1400 | 0.1573 | 0.9468 |
| 0.0228 | 1.04 | 1500 | 0.1156 | 0.9626 |
| 0.1261 | 1.11 | 1600 | 0.0628 | 0.9727 |
| 0.001 | 1.18 | 1700 | 0.0730 | 0.9770 |
| 0.0515 | 1.25 | 1800 | 0.1589 | 0.9468 |
| 0.0195 | 1.32 | 1900 | 0.1114 | 0.9641 |
| 0.0696 | 1.39 | 2000 | 0.1507 | 0.9555 |
| 0.0006 | 1.46 | 2100 | 0.0799 | 0.9741 |
| 0.0063 | 1.53 | 2200 | 0.0979 | 0.9684 |
| 0.0337 | 1.6 | 2300 | 0.1191 | 0.9598 |
| 0.0261 | 1.67 | 2400 | 0.0839 | 0.9727 |
| 0.001 | 1.74 | 2500 | 0.0911 | 0.9770 |
| 0.001 | 1.81 | 2600 | 0.0726 | 0.9799 |
| 0.0003 | 1.88 | 2700 | 0.0581 | 0.9842 |
| 0.0004 | 1.95 | 2800 | 0.0544 | 0.9856 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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