This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.8835
Accuracy: 0.7609

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: 1.5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
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: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.357	0.9565	11	1.3906	0.3913
1.2964	2.0	23	1.2819	0.4348
1.1609	2.9565	34	1.1804	0.4783
1.0747	4.0	46	1.0911	0.6087
1.027	4.9565	57	1.0176	0.6304
0.8985	6.0	69	0.8963	0.6739
0.8031	6.9565	80	0.9867	0.6739
0.7744	8.0	92	0.8710	0.6522
0.7488	8.9565	103	0.8845	0.6957
0.6767	10.0	115	0.8693	0.6957
0.6082	10.9565	126	0.8133	0.6739
0.6354	12.0	138	0.8771	0.6739
0.6422	12.9565	149	0.8137	0.7174
0.584	14.0	161	0.8861	0.6522
0.5763	14.9565	172	0.8459	0.7391
0.5238	16.0	184	0.8590	0.7174
0.528	16.9565	195	0.8705	0.7174
0.5626	18.0	207	0.8636	0.7174
0.5395	18.9565	218	0.8794	0.6957
0.4696	20.0	230	0.8835	0.7609
0.488	20.9565	241	0.8889	0.7391
0.4764	22.0	253	0.9109	0.7174
0.4668	22.9565	264	0.8893	0.7391
0.4676	24.0	276	0.9082	0.6957
0.4619	24.9565	287	0.9353	0.7174
0.4727	26.0	299	0.9331	0.7174
0.4461	26.9565	310	0.8937	0.7391
0.428	28.0	322	0.9175	0.7174
0.4694	28.9565	333	0.9340	0.6957
0.3812	30.0	345	0.9722	0.6739
0.4252	30.9565	356	0.9433	0.7174
0.3883	32.0	368	0.9420	0.7391
0.4228	32.9565	379	0.9483	0.6739
0.4288	34.0	391	0.9529	0.7174
0.3982	34.9565	402	0.9506	0.7174
0.3935	36.0	414	0.9539	0.6739
0.3974	36.9565	425	0.9599	0.6957
0.3893	38.0	437	0.9608	0.6957
0.4201	38.2609	440	0.9608	0.6957

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3

Augusto777/swinv2-tiny-patch4-window8-256-DMAE-da-colab2

swinv2-tiny-patch4-window8-256-DMAE-da-colab2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Augusto777/
swinv2-tiny-patch4-window8-256-DMAE-da-colab2