---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: urinary_carcinoma_classifier_g002
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train[:63]
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9230769230769231
---

<!-- 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. -->

# urinary_carcinoma_classifier_g002

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3544
- Accuracy: 0.9231

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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        | 1.0   | 1    | 0.6814          | 0.5385   |
| No log        | 2.0   | 2    | 0.6743          | 0.6923   |
| No log        | 3.0   | 3    | 0.6449          | 0.7692   |
| No log        | 4.0   | 4    | 0.6149          | 0.7692   |
| No log        | 5.0   | 5    | 0.5980          | 0.7692   |
| No log        | 6.0   | 6    | 0.5855          | 0.7692   |
| No log        | 7.0   | 7    | 0.5663          | 0.7692   |
| No log        | 8.0   | 8    | 0.5675          | 0.7692   |
| No log        | 9.0   | 9    | 0.5530          | 0.7692   |
| 0.637         | 10.0  | 10   | 0.5246          | 0.8462   |
| 0.637         | 11.0  | 11   | 0.5135          | 0.7692   |
| 0.637         | 12.0  | 12   | 0.5296          | 0.8462   |
| 0.637         | 13.0  | 13   | 0.5340          | 0.8462   |
| 0.637         | 14.0  | 14   | 0.4781          | 0.9231   |
| 0.637         | 15.0  | 15   | 0.4870          | 0.8462   |
| 0.637         | 16.0  | 16   | 0.4701          | 0.8462   |
| 0.637         | 17.0  | 17   | 0.4521          | 1.0      |
| 0.637         | 18.0  | 18   | 0.4266          | 0.9231   |
| 0.637         | 19.0  | 19   | 0.4220          | 0.9231   |
| 0.4474        | 20.0  | 20   | 0.3837          | 0.9231   |
| 0.4474        | 21.0  | 21   | 0.4257          | 0.8462   |
| 0.4474        | 22.0  | 22   | 0.4093          | 0.9231   |
| 0.4474        | 23.0  | 23   | 0.4019          | 1.0      |
| 0.4474        | 24.0  | 24   | 0.4578          | 0.8462   |
| 0.4474        | 25.0  | 25   | 0.3932          | 1.0      |
| 0.4474        | 26.0  | 26   | 0.3838          | 1.0      |
| 0.4474        | 27.0  | 27   | 0.3627          | 1.0      |
| 0.4474        | 28.0  | 28   | 0.3862          | 0.9231   |
| 0.4474        | 29.0  | 29   | 0.3624          | 0.9231   |
| 0.3102        | 30.0  | 30   | 0.3544          | 0.9231   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1