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---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
model-index:
- name: dit-base-finetuned-brs
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: 0.8235294117647058
- name: F1
type: f1
value: 0.8421052631578948
---
<!-- 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. -->
# dit-base-finetuned-brs
This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2679
- Accuracy: 0.8235
- F1: 0.8421
- Precision (ppv): 0.8889
- Recall (sensitivity): 0.8
- Specificity: 0.8571
- Npv: 0.75
- Auc: 0.8286
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision (ppv) | Recall (sensitivity) | Specificity | Npv | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
| 0.6951 | 6.25 | 100 | 0.7100 | 0.4706 | 0.4 | 0.6 | 0.3 | 0.7143 | 0.4167 | 0.5071 |
| 0.5094 | 12.49 | 200 | 0.6511 | 0.5294 | 0.6 | 0.6 | 0.6 | 0.4286 | 0.4286 | 0.5143 |
| 0.5338 | 18.74 | 300 | 0.6113 | 0.6471 | 0.6667 | 0.75 | 0.6 | 0.7143 | 0.5556 | 0.6571 |
| 0.444 | 24.98 | 400 | 0.7057 | 0.6471 | 0.625 | 0.8333 | 0.5 | 0.8571 | 0.5455 | 0.6786 |
| 0.3877 | 31.25 | 500 | 0.7836 | 0.7059 | 0.7368 | 0.7778 | 0.7 | 0.7143 | 0.625 | 0.7071 |
| 0.6238 | 37.49 | 600 | 0.8340 | 0.7059 | 0.6667 | 1.0 | 0.5 | 1.0 | 0.5833 | 0.75 |
| 0.6856 | 43.74 | 700 | 1.0278 | 0.7647 | 0.8000 | 0.8 | 0.8 | 0.7143 | 0.7143 | 0.7571 |
| 0.487 | 49.98 | 800 | 1.0279 | 0.7647 | 0.7778 | 0.875 | 0.7 | 0.8571 | 0.6667 | 0.7786 |
| 0.4039 | 56.25 | 900 | 0.9028 | 0.7647 | 0.7778 | 0.875 | 0.7 | 0.8571 | 0.6667 | 0.7786 |
| 0.2214 | 62.49 | 1000 | 0.6894 | 0.8235 | 0.8235 | 1.0 | 0.7 | 1.0 | 0.7 | 0.85 |
| 0.7441 | 68.74 | 1100 | 1.1261 | 0.8235 | 0.8421 | 0.8889 | 0.8 | 0.8571 | 0.75 | 0.8286 |
| 0.5714 | 74.98 | 1200 | 0.8956 | 0.8235 | 0.8235 | 1.0 | 0.7 | 1.0 | 0.7 | 0.85 |
| 0.3093 | 81.25 | 1300 | 1.2498 | 0.7059 | 0.7059 | 0.8571 | 0.6 | 0.8571 | 0.6 | 0.7286 |
| 0.6528 | 87.49 | 1400 | 1.6744 | 0.7647 | 0.7778 | 0.875 | 0.7 | 0.8571 | 0.6667 | 0.7786 |
| 0.3314 | 93.74 | 1500 | 1.8034 | 0.7059 | 0.7059 | 0.8571 | 0.6 | 0.8571 | 0.6 | 0.7286 |
| 0.3617 | 99.98 | 1600 | 1.2679 | 0.8235 | 0.8421 | 0.8889 | 0.8 | 0.8571 | 0.75 | 0.8286 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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