---
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: custom-cloud-model
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9506173133850098
---

<!-- 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. -->

# custom-cloud-model

This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2242
- Accuracy: 0.9506

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.153         | 1.0   | 80   | 0.9964          | 0.7510   |
| 0.8046        | 2.0   | 160  | 0.4400          | 0.8477   |
| 0.7706        | 3.0   | 240  | 0.5513          | 0.8107   |
| 0.6695        | 4.0   | 320  | 0.2639          | 0.9136   |
| 0.4697        | 5.0   | 400  | 0.2199          | 0.9300   |
| 0.4564        | 6.0   | 480  | 0.1808          | 0.9321   |
| 0.4094        | 7.0   | 560  | 0.2471          | 0.9239   |
| 0.2445        | 8.0   | 640  | 0.1998          | 0.9424   |
| 0.3153        | 9.0   | 720  | 0.2600          | 0.9136   |
| 0.1994        | 10.0  | 800  | 0.3160          | 0.9259   |
| 0.2205        | 11.0  | 880  | 0.2486          | 0.9239   |
| 0.2312        | 12.0  | 960  | 0.2131          | 0.9486   |
| 0.2382        | 13.0  | 1040 | 0.2487          | 0.9239   |
| 0.1158        | 14.0  | 1120 | 0.2153          | 0.9506   |
| 0.1           | 15.0  | 1200 | 0.2271          | 0.9486   |
| 0.0916        | 16.0  | 1280 | 0.2626          | 0.9259   |
| 0.1021        | 17.0  | 1360 | 0.2130          | 0.9403   |
| 0.0991        | 18.0  | 1440 | 0.2240          | 0.9444   |
| 0.0526        | 19.0  | 1520 | 0.2353          | 0.9506   |
| 0.0461        | 20.0  | 1600 | 0.2137          | 0.9506   |
| 0.0832        | 21.0  | 1680 | 0.2215          | 0.9444   |
| 0.0708        | 22.0  | 1760 | 0.2114          | 0.9506   |
| 0.1027        | 23.0  | 1840 | 0.2104          | 0.9527   |
| 0.0292        | 24.0  | 1920 | 0.2346          | 0.9547   |
| 0.033         | 25.0  | 2000 | 0.2242          | 0.9506   |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1