---
license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: swinv2-base-patch4-window8-256-finetuned-ind-17-imbalanced-aadhaarmask
  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: 0.8407833120476799
    - name: Recall
      type: recall
      value: 0.8407833120476799
    - name: F1
      type: f1
      value: 0.8382298834449193
    - name: Precision
      type: precision
      value: 0.8403613762272836
---

<!-- 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-base-patch4-window8-256-finetuned-ind-17-imbalanced-aadhaarmask

This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3672
- Accuracy: 0.8408
- Recall: 0.8408
- F1: 0.8382
- Precision: 0.8404

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Recall | F1     | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.6524        | 0.9974 | 293  | 0.5989          | 0.7986   | 0.7986 | 0.7886 | 0.7959    |
| 0.5004        | 1.9983 | 587  | 0.4830          | 0.8110   | 0.8110 | 0.8078 | 0.8190    |
| 0.3912        | 2.9991 | 881  | 0.4254          | 0.8199   | 0.8199 | 0.8162 | 0.8196    |
| 0.4007        | 4.0    | 1175 | 0.4324          | 0.8301   | 0.8301 | 0.8251 | 0.8302    |
| 0.2694        | 4.9974 | 1468 | 0.4215          | 0.8272   | 0.8272 | 0.8218 | 0.8301    |
| 0.3865        | 5.9983 | 1762 | 0.3620          | 0.8459   | 0.8459 | 0.8438 | 0.8471    |
| 0.2748        | 6.9991 | 2056 | 0.3733          | 0.8395   | 0.8395 | 0.8354 | 0.8510    |
| 0.3471        | 8.0    | 2350 | 0.3594          | 0.8370   | 0.8370 | 0.8364 | 0.8434    |
| 0.3361        | 8.9974 | 2643 | 0.3632          | 0.8404   | 0.8404 | 0.8386 | 0.8414    |
| 0.2399        | 9.9745 | 2930 | 0.3436          | 0.8455   | 0.8455 | 0.8446 | 0.8469    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.0a0+81ea7a4
- Datasets 2.19.0
- Tokenizers 0.19.1