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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-rim_one-new
results:
- task:
type: image-classification
name: Image Classification
dataset:
type: rimonedl
name: RIM ONE DL
split: test
metrics:
- type: f1
value: 0.9197860962566845
name: F1
- task:
type: image-classification
name: Image Classification
dataset:
type: rim one
name: RIMONEDL
split: test
metrics:
- type: precision
value: 0.9247311827956989
name: precision
- type: recall
value: 0.9148936170212766
name: Recall
- type: accuracy
value: 0.8972602739726028
name: Accuracy
- type: roc_auc
value: 0.8901391162029461
name: AUC
---
<!-- 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. -->
# beit-base-patch16-224-pt22k-ft22k-rim_one-new
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4550
- Accuracy: 0.8767
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.73 | 2 | 0.2411 | 0.9178 |
| No log | 1.73 | 4 | 0.2182 | 0.8973 |
| No log | 2.73 | 6 | 0.3085 | 0.8973 |
| No log | 3.73 | 8 | 0.2794 | 0.8973 |
| 0.1392 | 4.73 | 10 | 0.2398 | 0.9110 |
| 0.1392 | 5.73 | 12 | 0.2925 | 0.8973 |
| 0.1392 | 6.73 | 14 | 0.2798 | 0.9110 |
| 0.1392 | 7.73 | 16 | 0.2184 | 0.9178 |
| 0.1392 | 8.73 | 18 | 0.3007 | 0.9110 |
| 0.0416 | 9.73 | 20 | 0.3344 | 0.9041 |
| 0.0416 | 10.73 | 22 | 0.3626 | 0.9110 |
| 0.0416 | 11.73 | 24 | 0.4842 | 0.8904 |
| 0.0416 | 12.73 | 26 | 0.3664 | 0.8973 |
| 0.0416 | 13.73 | 28 | 0.3458 | 0.9110 |
| 0.0263 | 14.73 | 30 | 0.2810 | 0.9110 |
| 0.0263 | 15.73 | 32 | 0.4695 | 0.8699 |
| 0.0263 | 16.73 | 34 | 0.3723 | 0.9041 |
| 0.0263 | 17.73 | 36 | 0.3447 | 0.9041 |
| 0.0263 | 18.73 | 38 | 0.3708 | 0.8904 |
| 0.0264 | 19.73 | 40 | 0.4052 | 0.9110 |
| 0.0264 | 20.73 | 42 | 0.4492 | 0.9041 |
| 0.0264 | 21.73 | 44 | 0.4649 | 0.8904 |
| 0.0264 | 22.73 | 46 | 0.4061 | 0.9178 |
| 0.0264 | 23.73 | 48 | 0.4136 | 0.9110 |
| 0.0139 | 24.73 | 50 | 0.4183 | 0.8973 |
| 0.0139 | 25.73 | 52 | 0.4504 | 0.8904 |
| 0.0139 | 26.73 | 54 | 0.4368 | 0.8973 |
| 0.0139 | 27.73 | 56 | 0.4711 | 0.9110 |
| 0.0139 | 28.73 | 58 | 0.3928 | 0.9110 |
| 0.005 | 29.73 | 60 | 0.4550 | 0.8767 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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