---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sdss-cnn
  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. -->

# sdss-cnn

This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1573
- Accuracy: 0.9505

## 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: 0.0001
- train_batch_size: 100
- eval_batch_size: 100
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 80   | 0.4954          | 0.8635   |
| No log        | 2.0   | 160  | 0.2788          | 0.9055   |
| No log        | 3.0   | 240  | 0.2239          | 0.9085   |
| No log        | 4.0   | 320  | 0.1991          | 0.9325   |
| No log        | 5.0   | 400  | 0.1954          | 0.94     |
| No log        | 6.0   | 480  | 0.1854          | 0.9445   |
| 0.3543        | 7.0   | 560  | 0.1891          | 0.9375   |
| 0.3543        | 8.0   | 640  | 0.1777          | 0.943    |
| 0.3543        | 9.0   | 720  | 0.1780          | 0.9415   |
| 0.3543        | 10.0  | 800  | 0.1804          | 0.942    |
| 0.3543        | 11.0  | 880  | 0.1734          | 0.9475   |
| 0.3543        | 12.0  | 960  | 0.1689          | 0.947    |
| 0.2022        | 13.0  | 1040 | 0.1698          | 0.9445   |
| 0.2022        | 14.0  | 1120 | 0.1689          | 0.9405   |
| 0.2022        | 15.0  | 1200 | 0.1650          | 0.9475   |
| 0.2022        | 16.0  | 1280 | 0.1755          | 0.934    |
| 0.2022        | 17.0  | 1360 | 0.1635          | 0.944    |
| 0.2022        | 18.0  | 1440 | 0.1711          | 0.942    |
| 0.1836        | 19.0  | 1520 | 0.1604          | 0.9485   |
| 0.1836        | 20.0  | 1600 | 0.1595          | 0.95     |
| 0.1836        | 21.0  | 1680 | 0.1613          | 0.9475   |
| 0.1836        | 22.0  | 1760 | 0.1579          | 0.949    |
| 0.1836        | 23.0  | 1840 | 0.1593          | 0.946    |
| 0.1836        | 24.0  | 1920 | 0.1579          | 0.945    |
| 0.167         | 25.0  | 2000 | 0.1584          | 0.9495   |
| 0.167         | 26.0  | 2080 | 0.1573          | 0.9505   |
| 0.167         | 27.0  | 2160 | 0.1596          | 0.945    |
| 0.167         | 28.0  | 2240 | 0.1599          | 0.9435   |
| 0.167         | 29.0  | 2320 | 0.1565          | 0.9485   |
| 0.167         | 30.0  | 2400 | 0.1582          | 0.946    |
| 0.167         | 31.0  | 2480 | 0.1563          | 0.95     |
| 0.1568        | 32.0  | 2560 | 0.1563          | 0.95     |
| 0.1568        | 33.0  | 2640 | 0.1573          | 0.9495   |
| 0.1568        | 34.0  | 2720 | 0.1564          | 0.9465   |
| 0.1568        | 35.0  | 2800 | 0.1557          | 0.95     |
| 0.1568        | 36.0  | 2880 | 0.1554          | 0.949    |
| 0.1568        | 37.0  | 2960 | 0.1562          | 0.948    |
| 0.1515        | 38.0  | 3040 | 0.1555          | 0.948    |
| 0.1515        | 39.0  | 3120 | 0.1557          | 0.95     |
| 0.1515        | 40.0  | 3200 | 0.1559          | 0.9485   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3