---
license: other
base_model: neuralhaven/KDRSSC_TinyViT2MobileViT-xx-small
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: KDRSSC_TinyViT2MobileViT-xx-small-RESISC45_01
  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. -->

# KDRSSC_TinyViT2MobileViT-xx-small-RESISC45_01

This model is a fine-tuned version of [neuralhaven/KDRSSC_TinyViT2MobileViT-xx-small](https://huggingface.co./neuralhaven/KDRSSC_TinyViT2MobileViT-xx-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5369
- Accuracy: 0.8510
- Precision: 0.8555
- Recall: 0.8522
- F1: 0.8521

## 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: 0.0005
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.1561        | 1.0   | 20   | 0.8622          | 0.7856   | 0.8025    | 0.7860 | 0.7842 |
| 0.9463        | 2.0   | 40   | 0.7009          | 0.8127   | 0.8247    | 0.8136 | 0.8142 |
| 0.7867        | 3.0   | 60   | 0.7666          | 0.7817   | 0.8076    | 0.7824 | 0.7819 |
| 0.7504        | 4.0   | 80   | 0.6580          | 0.8203   | 0.8240    | 0.8214 | 0.8194 |
| 0.7147        | 5.0   | 100  | 0.6410          | 0.8208   | 0.8348    | 0.8204 | 0.8221 |
| 0.6715        | 6.0   | 120  | 0.5923          | 0.8343   | 0.8390    | 0.8360 | 0.8356 |
| 0.6114        | 7.0   | 140  | 0.5965          | 0.8321   | 0.8415    | 0.8338 | 0.8330 |
| 0.6041        | 8.0   | 160  | 0.5625          | 0.8440   | 0.8522    | 0.8448 | 0.8456 |
| 0.5449        | 9.0   | 180  | 0.5466          | 0.8457   | 0.8508    | 0.8470 | 0.8472 |
| 0.5607        | 10.0  | 200  | 0.5369          | 0.8510   | 0.8555    | 0.8522 | 0.8521 |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1