---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: microsoft_swinv2-tiny-patch4-window8-256-batch_16_epoch_4_classes_24_final_withAug
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: bengali_food_images
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9456521739130435
---

<!-- 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. -->

# microsoft_swinv2-tiny-patch4-window8-256-batch_16_epoch_4_classes_24_final_withAug

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the bengali_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2321
- Accuracy: 0.9457

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7162        | 0.09  | 100  | 1.4225          | 0.7079   |
| 1.2286        | 0.17  | 200  | 0.9461          | 0.7935   |
| 1.0323        | 0.26  | 300  | 0.7366          | 0.8356   |
| 0.8678        | 0.34  | 400  | 0.6211          | 0.8628   |
| 0.7849        | 0.43  | 500  | 0.5354          | 0.8655   |
| 0.7105        | 0.51  | 600  | 0.4793          | 0.8899   |
| 0.6198        | 0.6   | 700  | 0.4319          | 0.9090   |
| 0.6276        | 0.68  | 800  | 0.4022          | 0.8981   |
| 0.5411        | 0.77  | 900  | 0.3816          | 0.9117   |
| 0.4984        | 0.85  | 1000 | 0.3824          | 0.9022   |
| 0.5665        | 0.94  | 1100 | 0.3460          | 0.9212   |
| 0.5741        | 1.02  | 1200 | 0.3336          | 0.9158   |
| 0.4039        | 1.11  | 1300 | 0.3204          | 0.9130   |
| 0.4347        | 1.19  | 1400 | 0.3038          | 0.9307   |
| 0.3639        | 1.28  | 1500 | 0.2955          | 0.9253   |
| 0.4282        | 1.36  | 1600 | 0.2948          | 0.9293   |
| 0.4375        | 1.45  | 1700 | 0.2868          | 0.9212   |
| 0.3063        | 1.53  | 1800 | 0.2861          | 0.9334   |
| 0.3549        | 1.62  | 1900 | 0.2826          | 0.9293   |
| 0.4326        | 1.71  | 2000 | 0.2698          | 0.9348   |
| 0.3697        | 1.79  | 2100 | 0.2602          | 0.9280   |
| 0.3155        | 1.88  | 2200 | 0.2523          | 0.9361   |
| 0.3348        | 1.96  | 2300 | 0.2506          | 0.9470   |
| 0.3854        | 2.05  | 2400 | 0.2565          | 0.9321   |
| 0.3951        | 2.13  | 2500 | 0.2482          | 0.9402   |
| 0.3531        | 2.22  | 2600 | 0.2455          | 0.9402   |
| 0.3643        | 2.3   | 2700 | 0.2513          | 0.9375   |
| 0.3393        | 2.39  | 2800 | 0.2492          | 0.9429   |
| 0.3635        | 2.47  | 2900 | 0.2394          | 0.9402   |
| 0.3624        | 2.56  | 3000 | 0.2425          | 0.9389   |
| 0.3608        | 2.64  | 3100 | 0.2390          | 0.9457   |
| 0.3215        | 2.73  | 3200 | 0.2483          | 0.9321   |
| 0.2971        | 2.81  | 3300 | 0.2455          | 0.9402   |
| 0.3838        | 2.9   | 3400 | 0.2363          | 0.9470   |
| 0.3036        | 2.98  | 3500 | 0.2422          | 0.9402   |
| 0.401         | 3.07  | 3600 | 0.2398          | 0.9429   |
| 0.3458        | 3.15  | 3700 | 0.2517          | 0.9429   |
| 0.2908        | 3.24  | 3800 | 0.2423          | 0.9457   |
| 0.3016        | 3.32  | 3900 | 0.2402          | 0.9443   |
| 0.2961        | 3.41  | 4000 | 0.2414          | 0.9457   |
| 0.3822        | 3.5   | 4100 | 0.2413          | 0.9416   |
| 0.2596        | 3.58  | 4200 | 0.2356          | 0.9457   |
| 0.3064        | 3.67  | 4300 | 0.2324          | 0.9497   |
| 0.3059        | 3.75  | 4400 | 0.2321          | 0.9457   |
| 0.42          | 3.84  | 4500 | 0.2556          | 0.9402   |
| 0.2959        | 3.92  | 4600 | 0.2491          | 0.9416   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2