---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: image-quality-mobilenetv3
  results: []
base_model:
- timm/mobilenetv3_large_100.ra_in1k
pipeline_tag: image-classification
---

<!-- 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. -->

# image-quality-mobilenetv3

This model is a fine-tuned version of [](https://huggingface.co./) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0123

## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.3424        | 1.0   | 36   | 3.0847          |
| 0.6173        | 2.0   | 72   | 0.4851          |
| 0.1393        | 3.0   | 108  | 0.0988          |
| 0.0575        | 4.0   | 144  | 0.0536          |
| 0.0388        | 5.0   | 180  | 0.0377          |
| 0.0324        | 6.0   | 216  | 0.0320          |
| 0.0291        | 7.0   | 252  | 0.0312          |
| 0.0255        | 8.0   | 288  | 0.0266          |
| 0.023         | 9.0   | 324  | 0.0232          |
| 0.0213        | 10.0  | 360  | 0.0214          |
| 0.0205        | 11.0  | 396  | 0.0209          |
| 0.0193        | 12.0  | 432  | 0.0198          |
| 0.0183        | 13.0  | 468  | 0.0191          |
| 0.0185        | 14.0  | 504  | 0.0179          |
| 0.0175        | 15.0  | 540  | 0.0171          |
| 0.0166        | 16.0  | 576  | 0.0186          |
| 0.0161        | 17.0  | 612  | 0.0167          |
| 0.0164        | 18.0  | 648  | 0.0163          |
| 0.0152        | 19.0  | 684  | 0.0160          |
| 0.0149        | 20.0  | 720  | 0.0156          |
| 0.0151        | 21.0  | 756  | 0.0159          |
| 0.0147        | 22.0  | 792  | 0.0153          |
| 0.0154        | 23.0  | 828  | 0.0162          |
| 0.0147        | 24.0  | 864  | 0.0150          |
| 0.0144        | 25.0  | 900  | 0.0147          |
| 0.0143        | 26.0  | 936  | 0.0144          |
| 0.0144        | 27.0  | 972  | 0.0139          |
| 0.0152        | 28.0  | 1008 | 0.0150          |
| 0.0129        | 29.0  | 1044 | 0.0134          |
| 0.0128        | 30.0  | 1080 | 0.0135          |
| 0.0126        | 31.0  | 1116 | 0.0141          |
| 0.0131        | 32.0  | 1152 | 0.0145          |
| 0.0133        | 33.0  | 1188 | 0.0131          |
| 0.0124        | 34.0  | 1224 | 0.0133          |
| 0.013         | 35.0  | 1260 | 0.0148          |
| 0.0121        | 36.0  | 1296 | 0.0129          |
| 0.0116        | 37.0  | 1332 | 0.0127          |
| 0.0124        | 38.0  | 1368 | 0.0129          |
| 0.0121        | 39.0  | 1404 | 0.0134          |
| 0.0121        | 40.0  | 1440 | 0.0128          |
| 0.0119        | 41.0  | 1476 | 0.0126          |
| 0.0116        | 42.0  | 1512 | 0.0125          |
| 0.0118        | 43.0  | 1548 | 0.0126          |
| 0.0114        | 44.0  | 1584 | 0.0127          |
| 0.0117        | 45.0  | 1620 | 0.0125          |
| 0.0116        | 46.0  | 1656 | 0.0127          |
| 0.0118        | 47.0  | 1692 | 0.0126          |
| 0.0116        | 48.0  | 1728 | 0.0123          |
| 0.0114        | 49.0  | 1764 | 0.0123          |
| 0.0113        | 50.0  | 1800 | 0.0123          |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3