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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_fold5
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: 0.8448818098813027
---
<!-- 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_fold5
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: 1.4092
- Accuracy: 0.8449
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.434 | 1.0 | 2468 | 0.4027 | 0.8307 |
| 0.321 | 2.0 | 4936 | 0.3800 | 0.8422 |
| 0.2658 | 3.0 | 7404 | 0.3919 | 0.8538 |
| 0.1883 | 4.0 | 9872 | 0.5137 | 0.8496 |
| 0.1083 | 5.0 | 12340 | 0.6774 | 0.8501 |
| 0.1819 | 6.0 | 14808 | 0.9184 | 0.8469 |
| 0.1208 | 7.0 | 17276 | 1.1502 | 0.8448 |
| 0.1339 | 8.0 | 19744 | 1.3133 | 0.8418 |
| 0.0217 | 9.0 | 22212 | 1.3895 | 0.8434 |
| 0.0057 | 10.0 | 24680 | 1.4092 | 0.8449 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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