---
library_name: transformers
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-finetuned-lf-invalidation
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.676595744680851
---

<!-- 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-tiny-patch4-window8-256-finetuned-lf-invalidation

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7236
- Accuracy: 0.6766

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.5608        | 0.9796  | 12   | 0.6779          | 0.5532   |
| 0.5249        | 1.9592  | 24   | 0.5234          | 0.7617   |
| 0.4404        | 2.9388  | 36   | 0.5121          | 0.7766   |
| 0.3893        | 4.0     | 49   | 0.3981          | 0.8128   |
| 0.4083        | 4.9796  | 61   | 0.5870          | 0.6447   |
| 0.3725        | 5.9592  | 73   | 0.4991          | 0.7553   |
| 0.3909        | 6.9388  | 85   | 0.4062          | 0.8426   |
| 0.3799        | 8.0     | 98   | 0.5115          | 0.7574   |
| 0.3332        | 8.9796  | 110  | 0.4470          | 0.8277   |
| 0.3108        | 9.9592  | 122  | 0.3451          | 0.8681   |
| 0.308         | 10.9388 | 134  | 0.5822          | 0.7511   |
| 0.3699        | 12.0    | 147  | 0.4653          | 0.8106   |
| 0.2945        | 12.9796 | 159  | 0.4171          | 0.8426   |
| 0.2934        | 13.9592 | 171  | 0.4366          | 0.8234   |
| 0.2719        | 14.9388 | 183  | 0.5905          | 0.7638   |
| 0.3287        | 16.0    | 196  | 0.6654          | 0.7234   |
| 0.271         | 16.9796 | 208  | 0.6328          | 0.7447   |
| 0.3018        | 17.9592 | 220  | 0.4671          | 0.8255   |
| 0.2763        | 18.9388 | 232  | 0.6032          | 0.7468   |
| 0.2834        | 20.0    | 245  | 0.7016          | 0.7      |
| 0.2653        | 20.9796 | 257  | 0.4089          | 0.8468   |
| 0.2666        | 21.9592 | 269  | 0.7905          | 0.6447   |
| 0.2941        | 22.9388 | 281  | 0.6064          | 0.7553   |
| 0.2792        | 24.0    | 294  | 0.7444          | 0.7085   |
| 0.2019        | 24.9796 | 306  | 0.7595          | 0.7170   |
| 0.2552        | 25.9592 | 318  | 1.0296          | 0.5660   |
| 0.2451        | 26.9388 | 330  | 0.5999          | 0.7340   |
| 0.2126        | 28.0    | 343  | 0.5730          | 0.7660   |
| 0.2214        | 28.9796 | 355  | 0.9756          | 0.5809   |
| 0.2633        | 29.9592 | 367  | 0.4134          | 0.8404   |
| 0.2427        | 30.9388 | 379  | 0.8228          | 0.6362   |
| 0.2405        | 32.0    | 392  | 0.5279          | 0.7723   |
| 0.2078        | 32.9796 | 404  | 0.6581          | 0.6979   |
| 0.2201        | 33.9592 | 416  | 0.9132          | 0.5745   |
| 0.2481        | 34.9388 | 428  | 0.9526          | 0.5617   |
| 0.248         | 36.0    | 441  | 0.8979          | 0.5553   |
| 0.2209        | 36.9796 | 453  | 0.8351          | 0.5915   |
| 0.2253        | 37.9592 | 465  | 0.6744          | 0.6851   |
| 0.2447        | 38.9388 | 477  | 0.7794          | 0.6404   |
| 0.2049        | 40.0    | 490  | 0.6136          | 0.7468   |
| 0.1965        | 40.9796 | 502  | 0.6582          | 0.7340   |
| 0.2638        | 41.9592 | 514  | 0.7487          | 0.6766   |
| 0.2297        | 42.9388 | 526  | 0.7282          | 0.6702   |
| 0.2163        | 44.0    | 539  | 0.5713          | 0.7511   |
| 0.2016        | 44.9796 | 551  | 0.5994          | 0.7319   |
| 0.1739        | 45.9592 | 563  | 0.6865          | 0.6915   |
| 0.2497        | 46.9388 | 575  | 0.6901          | 0.6957   |
| 0.2293        | 48.0    | 588  | 0.7150          | 0.6851   |
| 0.2237        | 48.9796 | 600  | 0.7236          | 0.6766   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1