---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-large-patch4-window12-192-22k-finetuned-eurosat-50
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: Skin_Cancer
      split: train
      args: Skin_Cancer
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7220338983050848
---

<!-- 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. -->

# swinv2-large-patch4-window12-192-22k-finetuned-eurosat-50

This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-large-patch4-window12-192-22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6967
- Accuracy: 0.7220

## 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-06
- 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: 0.005
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.97  | 9    | 1.6984          | 0.3729   |
| No log        | 1.95  | 18   | 1.5150          | 0.4881   |
| 1.6944        | 2.92  | 27   | 1.3304          | 0.5390   |
| 1.6944        | 4.0   | 37   | 1.1761          | 0.6      |
| 1.3633        | 4.97  | 46   | 1.0588          | 0.6373   |
| 1.3633        | 5.95  | 55   | 0.9952          | 0.6475   |
| 1.1208        | 6.92  | 64   | 0.9326          | 0.6610   |
| 1.1208        | 8.0   | 74   | 0.8785          | 0.6712   |
| 0.9891        | 8.97  | 83   | 0.8478          | 0.6746   |
| 0.9891        | 9.95  | 92   | 0.8144          | 0.6847   |
| 0.9011        | 10.92 | 101  | 0.7774          | 0.7017   |
| 0.9011        | 12.0  | 111  | 0.7567          | 0.6983   |
| 0.8143        | 12.97 | 120  | 0.7525          | 0.6949   |
| 0.8143        | 13.95 | 129  | 0.7309          | 0.7051   |
| 0.8143        | 14.92 | 138  | 0.7141          | 0.7119   |
| 0.7926        | 16.0  | 148  | 0.7095          | 0.7186   |
| 0.7926        | 16.97 | 157  | 0.7057          | 0.7220   |
| 0.7439        | 17.95 | 166  | 0.6988          | 0.7220   |
| 0.7439        | 18.92 | 175  | 0.6967          | 0.7220   |
| 0.7533        | 19.46 | 180  | 0.6967          | 0.7220   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3