swin-tiny-patch4-window7-224-finetuned-tekno24-highdata-90-2nd

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.9708
• Accuracy: 0.6406
• F1: 0.6352
• Precision: 0.6327
• Recall: 0.6406

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
1.1525	0.9908	81	1.0163	0.5300	0.5131	0.5061	0.5300
1.0727	1.9939	163	1.0486	0.5392	0.5058	0.5742	0.5392
1.0178	2.9969	245	1.0716	0.5207	0.4967	0.5015	0.5207
1.104	4.0	327	1.0413	0.5346	0.5035	0.5109	0.5346
1.126	4.9908	408	1.0294	0.5622	0.5281	0.5507	0.5622
1.1405	5.9939	490	1.0851	0.5484	0.5454	0.6048	0.5484
1.1432	6.9969	572	1.0160	0.5668	0.5442	0.5949	0.5668
1.1034	8.0	654	0.9752	0.5714	0.5517	0.5587	0.5714
1.0972	8.9908	735	0.9797	0.5714	0.5567	0.5755	0.5714
1.0691	9.9939	817	0.9662	0.5899	0.5869	0.6135	0.5899
1.1044	10.9969	899	0.9390	0.6175	0.5946	0.6186	0.6175
1.0781	12.0	981	0.9324	0.6129	0.6052	0.6175	0.6129
1.1094	12.9908	1062	0.9388	0.5853	0.5533	0.5572	0.5853
1.0778	13.9939	1144	0.9288	0.5853	0.5627	0.5752	0.5853
1.0594	14.9969	1226	0.9325	0.5853	0.5778	0.6006	0.5853
1.0903	16.0	1308	0.9501	0.6175	0.6069	0.6074	0.6175
1.0076	16.9908	1389	0.9386	0.5991	0.5876	0.5987	0.5991
1.0427	17.9939	1471	0.9403	0.5899	0.5736	0.5916	0.5899
1.0086	18.9969	1553	0.9690	0.5899	0.5813	0.5824	0.5899
1.0063	20.0	1635	0.9788	0.5760	0.5836	0.5966	0.5760
0.9835	20.9908	1716	0.9336	0.6129	0.6017	0.6138	0.6129
1.0145	21.9939	1798	0.9641	0.5945	0.5870	0.5934	0.5945
0.953	22.9969	1880	0.9869	0.6129	0.5942	0.6033	0.6129
0.9429	24.0	1962	0.9708	0.6406	0.6352	0.6327	0.6406
1.0114	24.9908	2043	0.9797	0.5760	0.5666	0.5677	0.5760
0.9788	25.9939	2125	0.9722	0.6037	0.6012	0.6047	0.6037
0.9991	26.9969	2207	0.9647	0.6221	0.6122	0.6163	0.6221
0.8959	28.0	2289	0.9737	0.6083	0.6030	0.6093	0.6083
0.9705	28.9908	2370	0.9634	0.6129	0.6077	0.6147	0.6129
0.9132	29.7248	2430	0.9626	0.6129	0.6073	0.6102	0.6129

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1