---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-large-patch4-window12to16-192to256-22kto1k-ft-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: 0.8338983050847457
---

<!-- 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-window12to16-192to256-22kto1k-ft-finetuned-eurosat-50

This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5108
- Accuracy: 0.8339

## 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: 3e-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: 0.005
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.97  | 18   | 1.3358          | 0.5017   |
| 1.7327        | 2.0   | 37   | 0.9711          | 0.6102   |
| 1.1314        | 2.97  | 55   | 0.6877          | 0.7254   |
| 0.7956        | 4.0   | 74   | 0.6924          | 0.7458   |
| 0.6511        | 4.97  | 92   | 0.7236          | 0.6915   |
| 0.5609        | 6.0   | 111  | 0.5625          | 0.8169   |
| 0.4585        | 6.97  | 129  | 0.5356          | 0.8102   |
| 0.3988        | 8.0   | 148  | 0.8137          | 0.7186   |
| 0.35          | 8.97  | 166  | 0.5569          | 0.8136   |
| 0.3431        | 10.0  | 185  | 0.6979          | 0.7729   |
| 0.2888        | 10.97 | 203  | 0.5444          | 0.8      |
| 0.2553        | 12.0  | 222  | 0.6462          | 0.7729   |
| 0.2263        | 12.97 | 240  | 0.5093          | 0.8373   |
| 0.2263        | 14.0  | 259  | 0.5331          | 0.8169   |
| 0.2323        | 14.97 | 277  | 0.5521          | 0.8203   |
| 0.1601        | 16.0  | 296  | 0.5984          | 0.7831   |
| 0.1645        | 16.97 | 314  | 0.6850          | 0.7932   |
| 0.202         | 18.0  | 333  | 0.5786          | 0.8      |
| 0.1762        | 18.97 | 351  | 0.5961          | 0.8305   |
| 0.1546        | 20.0  | 370  | 0.6169          | 0.8373   |
| 0.1583        | 20.97 | 388  | 0.4907          | 0.8373   |
| 0.1168        | 22.0  | 407  | 0.4846          | 0.8508   |
| 0.1193        | 22.97 | 425  | 0.5030          | 0.8475   |
| 0.1275        | 24.0  | 444  | 0.5287          | 0.8373   |
| 0.1214        | 24.97 | 462  | 0.5240          | 0.8407   |
| 0.1107        | 26.0  | 481  | 0.5439          | 0.8407   |
| 0.1107        | 26.97 | 499  | 0.4901          | 0.8305   |
| 0.0921        | 28.0  | 518  | 0.5037          | 0.8407   |
| 0.1105        | 28.97 | 536  | 0.5105          | 0.8305   |
| 0.0883        | 29.19 | 540  | 0.5108          | 0.8339   |


### Framework versions

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