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---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Karma_3Class_RMSprop_1-e5_10Epoch_Beit-base-patch16_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.843291881508442
---
<!-- 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. -->
# Karma_3Class_RMSprop_1-e5_10Epoch_Beit-base-patch16_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4322
- Accuracy: 0.8433
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3441 | 1.0 | 2466 | 0.3930 | 0.8392 |
| 0.3299 | 2.0 | 4932 | 0.3836 | 0.8474 |
| 0.2436 | 3.0 | 7398 | 0.4043 | 0.8499 |
| 0.2235 | 4.0 | 9864 | 0.5186 | 0.8408 |
| 0.178 | 5.0 | 12330 | 0.6810 | 0.8485 |
| 0.1455 | 6.0 | 14796 | 0.8839 | 0.8442 |
| 0.1409 | 7.0 | 17262 | 1.1134 | 0.8424 |
| 0.181 | 8.0 | 19728 | 1.3633 | 0.8364 |
| 0.0965 | 9.0 | 22194 | 1.4369 | 0.8388 |
| 0.0314 | 10.0 | 24660 | 1.4322 | 0.8433 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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