---
license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window12-192-22k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Paper_Compared-swinv2-base
  results: []
---

<!-- 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. -->

# Paper_Compared-swinv2-base

This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-base-patch4-window12-192-22k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7159
- Accuracy: 0.8533

## 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: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.6917        | 0.9492 | 14   | 0.7844          | 0.7562   |
| 0.7734        | 1.9661 | 29   | 0.4380          | 0.8521   |
| 0.1927        | 2.9831 | 44   | 0.4694          | 0.8544   |
| 0.0956        | 4.0    | 59   | 0.6487          | 0.8251   |
| 0.0638        | 4.9492 | 73   | 0.6688          | 0.8296   |
| 0.0343        | 5.9661 | 88   | 0.7615          | 0.8352   |
| 0.0182        | 6.9831 | 103  | 0.7470          | 0.8352   |
| 0.038         | 8.0    | 118  | 0.7666          | 0.8465   |
| 0.0057        | 8.9492 | 132  | 0.7086          | 0.8454   |
| 0.0062        | 9.4915 | 140  | 0.7159          | 0.8533   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1