---
library_name: transformers
tags:
- multitask-learning
- efficientnet
- computer-vision
- generated_from_trainer
model-index:
- name: EfficientNetV1-B4-FacesMTL-EXP1
  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. -->

# EfficientNetV1-B4-FacesMTL-EXP1

This model is a fine-tuned version of EfficientNetV2-s on faces-mtl.
It achieves the following results on the evaluation set:
- Gender Accuracy: 0.9006
- Gender F1: 0.8651
- Age Mae: 6.7354
- Age Rmse: 9.1662
- Loss: 84.2761

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Gender Accuracy | Gender F1 | Age Mae | Age Rmse | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:|:--------:|:---------------:|
| 221.2506      | 0.1728 | 150  | 0.8704          | 0.7993    | 10.4148 | 13.8506  | 192.2673        |
| 163.1323      | 0.3456 | 300  | 0.8637          | 0.7814    | 9.4884  | 12.8120  | 164.4855        |
| 188.8794      | 0.5184 | 450  | 0.8680          | 0.8299    | 9.0322  | 12.2782  | 151.1256        |
| 147.5274      | 0.6912 | 600  | 0.8741          | 0.8056    | 8.1886  | 10.9624  | 120.4869        |
| 121.8239      | 0.8641 | 750  | 0.8879          | 0.8379    | 7.8131  | 10.4025  | 108.5096        |
| 110.5511      | 1.0369 | 900  | 0.8856          | 0.8386    | 7.7040  | 10.3324  | 107.0565        |
| 116.5555      | 1.2097 | 1050 | 0.8741          | 0.8046    | 7.4726  | 9.9674   | 99.6567         |
| 126.6334      | 1.3825 | 1200 | 0.8902          | 0.8415    | 7.5891  | 10.3014  | 106.3999        |
| 147.6252      | 1.5553 | 1350 | 0.8911          | 0.8509    | 7.3201  | 9.8158   | 96.6354         |
| 124.724       | 1.7281 | 1500 | 0.8951          | 0.8546    | 7.2476  | 9.6878   | 94.1328         |
| 107.5         | 1.9009 | 1650 | 0.8897          | 0.8372    | 7.0946  | 9.4325   | 89.2502         |
| 91.8285       | 2.0737 | 1800 | 0.8980          | 0.8612    | 7.0833  | 9.5193   | 90.9008         |
| 94.1933       | 2.2465 | 1950 | 0.8871          | 0.8302    | 7.0344  | 9.4989   | 90.5074         |
| 98.9504       | 2.4194 | 2100 | 0.8928          | 0.8459    | 6.9311  | 9.3160   | 87.0540         |
| 94.4654       | 2.5922 | 2250 | 0.8977          | 0.8611    | 6.9284  | 9.3570   | 87.8282         |
| 85.7435       | 2.7650 | 2400 | 0.8983          | 0.8634    | 6.8776  | 9.3332   | 87.3804         |
| 125.3979      | 2.9378 | 2550 | 0.8989          | 0.8589    | 6.8158  | 9.2204   | 85.2777         |
| 79.05         | 3.1106 | 2700 | 0.8977          | 0.8555    | 6.8892  | 9.3617   | 87.9025         |
| 81.3652       | 3.2834 | 2850 | 0.8954          | 0.8492    | 6.7664  | 9.1391   | 83.7815         |
| 82.3679       | 3.4562 | 3000 | 0.8989          | 0.8641    | 6.8370  | 9.2874   | 86.5219         |
| 83.2362       | 3.6290 | 3150 | 0.8951          | 0.8620    | 6.7723  | 9.1703   | 84.3717         |
| 80.1852       | 3.8018 | 3300 | 0.8995          | 0.8652    | 6.6909  | 9.0639   | 82.4177         |
| 111.4015      | 3.9747 | 3450 | 0.9012          | 0.8645    | 6.7183  | 9.1129   | 83.3005         |
| 76.6393       | 4.1475 | 3600 | 0.9018          | 0.8635    | 6.7800  | 9.1985   | 84.8666         |
| 80.495        | 4.3203 | 3750 | 0.9023          | 0.8673    | 6.6952  | 9.0644   | 82.4208         |
| 104.2716      | 4.4931 | 3900 | 0.9023          | 0.8655    | 6.7867  | 9.2289   | 85.4275         |
| 77.721        | 4.6659 | 4050 | 0.9020          | 0.8674    | 6.9182  | 9.4068   | 88.7482         |
| 70.1717       | 4.8387 | 4200 | 0.9009          | 0.8621    | 6.7354  | 9.1496   | 83.9694         |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.9.0+cu130
- Datasets 4.4.1
- Tokenizers 0.22.1