---
license: apache-2.0
base_model: NiharGupte/swin-tiny-patch4-window7-224-finetuned-student_six_classes
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-student_six_classes-finetuned-student_six_classes
  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: 0.83
---

<!-- 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. -->

# swin-tiny-patch4-window7-224-finetuned-student_six_classes-finetuned-student_six_classes

This model is a fine-tuned version of [NiharGupte/swin-tiny-patch4-window7-224-finetuned-student_six_classes](https://huggingface.co/NiharGupte/swin-tiny-patch4-window7-224-finetuned-student_six_classes) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4176
- Accuracy: 0.83

## 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: 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: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9231  | 3    | 0.4943          | 0.78     |
| No log        | 1.8462  | 6    | 0.4716          | 0.78     |
| No log        | 2.7692  | 9    | 0.4725          | 0.81     |
| 0.3732        | 4.0     | 13   | 0.4678          | 0.78     |
| 0.3732        | 4.9231  | 16   | 0.4779          | 0.78     |
| 0.3732        | 5.8462  | 19   | 0.4564          | 0.79     |
| 0.3459        | 6.7692  | 22   | 0.4556          | 0.82     |
| 0.3459        | 8.0     | 26   | 0.4757          | 0.77     |
| 0.3459        | 8.9231  | 29   | 0.4773          | 0.77     |
| 0.3273        | 9.8462  | 32   | 0.4661          | 0.77     |
| 0.3273        | 10.7692 | 35   | 0.4518          | 0.79     |
| 0.3273        | 12.0    | 39   | 0.4405          | 0.81     |
| 0.2974        | 12.9231 | 42   | 0.4359          | 0.82     |
| 0.2974        | 13.8462 | 45   | 0.4298          | 0.82     |
| 0.2974        | 14.7692 | 48   | 0.4242          | 0.84     |
| 0.2874        | 16.0    | 52   | 0.4199          | 0.84     |
| 0.2874        | 16.9231 | 55   | 0.4185          | 0.83     |
| 0.2874        | 17.8462 | 58   | 0.4179          | 0.83     |
| 0.2737        | 18.4615 | 60   | 0.4176          | 0.83     |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1