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---
license: apache-2.0
base_model: DunnBC22/vit-base-patch16-224-in21k_covid_19_ct_scans
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit-base-patch16-224-in21k_covid_19_ct_scans-finetuned-RCC
results: []
---
<!-- 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. -->
# vit-base-patch16-224-in21k_covid_19_ct_scans-finetuned-RCC
This model is a fine-tuned version of [DunnBC22/vit-base-patch16-224-in21k_covid_19_ct_scans](https://huggingface.co/DunnBC22/vit-base-patch16-224-in21k_covid_19_ct_scans) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3235
- Accuracy: 0.9032
- Precision: 0.9032
- Recall: 1.0
- F1: 0.4746
- Auc: 0.5
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:---:|
| No log | 1.0 | 7 | 0.3327 | 0.9032 | 0.9032 | 1.0 | 0.4746 | 0.5 |
| 0.3866 | 2.0 | 14 | 0.3213 | 0.9032 | 0.9032 | 1.0 | 0.4746 | 0.5 |
| 0.2647 | 3.0 | 21 | 0.3226 | 0.9032 | 0.9032 | 1.0 | 0.4746 | 0.5 |
| 0.2647 | 4.0 | 28 | 0.3246 | 0.9032 | 0.9032 | 1.0 | 0.4746 | 0.5 |
| 0.2593 | 5.0 | 35 | 0.3235 | 0.9032 | 0.9032 | 1.0 | 0.4746 | 0.5 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
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