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---
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
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