EfficientNetV1-B4-FacesMTL-EXP1

thethinkmachine/EfficientNetV1-B4-FacesMTL-EXP1

EfficientNetV1-B4-FacesMTL-EXP1 is a machine learning model from thethinkmachine. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
Model type: facesmultitasknet
Access: Public
Created: Nov 12, 2025
Updated: Nov 12, 2025
SHA: fa941134

Model card

From the Hugging Face model README

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

Library: transformers
Type: facesmultitasknet
Created: Nov 12, 2025
Updated: Nov 12, 2025