resnet-50-finetuned-barkley

This model is a fine-tuned version of microsoft/resnet-50 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.9221
• Precision: 0.8780
• Recall: 0.8618
• F1: 0.8574
• Accuracy: 0.8744
• Top1 Accuracy: 0.8618
• Error Rate: 0.1256

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: 0.0002
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Top1 Accuracy	Error Rate
1.6171	1.0	38	1.6195	0.0663	0.1513	0.0664	0.1738	0.1513	0.8262
1.6149	2.0	76	1.6160	0.2953	0.1579	0.0802	0.1785	0.1579	0.8215
1.6119	3.0	114	1.6112	0.0804	0.1579	0.0834	0.1772	0.1579	0.8228
1.6041	4.0	152	1.6015	0.4161	0.1974	0.1461	0.2155	0.1974	0.7845
1.5945	5.0	190	1.5895	0.4089	0.2895	0.2428	0.3092	0.2895	0.6908
1.5777	6.0	228	1.5710	0.5764	0.4408	0.3944	0.4663	0.4408	0.5337
1.561	7.0	266	1.5490	0.6013	0.4934	0.4516	0.5173	0.5	0.4827
1.536	8.0	304	1.5222	0.6377	0.5132	0.4711	0.5450	0.5132	0.4550
1.5081	9.0	342	1.4912	0.7595	0.5987	0.5869	0.6250	0.5987	0.3750
1.4756	10.0	380	1.4566	0.7579	0.6447	0.6293	0.6683	0.6447	0.3317
1.4387	11.0	418	1.4156	0.7914	0.6776	0.6692	0.6985	0.6776	0.3015
1.3993	12.0	456	1.3737	0.7997	0.6842	0.6732	0.7080	0.6842	0.2920
1.358	13.0	494	1.3288	0.8290	0.7039	0.7048	0.7232	0.7039	0.2768
1.3139	14.0	532	1.2806	0.8277	0.7434	0.7373	0.7592	0.75	0.2408
1.262	15.0	570	1.2345	0.8478	0.7697	0.7664	0.7829	0.7697	0.2171
1.2184	16.0	608	1.1887	0.8323	0.7697	0.7654	0.7818	0.7697	0.2182
1.1803	17.0	646	1.1408	0.8423	0.7763	0.7735	0.7931	0.7763	0.2069
1.1422	18.0	684	1.0966	0.8594	0.8158	0.8100	0.8317	0.8158	0.1683
1.1032	19.0	722	1.0587	0.8431	0.8026	0.7969	0.8145	0.8026	0.1855
1.058	20.0	760	1.0289	0.8610	0.8355	0.8301	0.8487	0.8355	0.1513
1.0252	21.0	798	0.9918	0.8576	0.8421	0.8370	0.8534	0.8421	0.1466
1.002	22.0	836	0.9727	0.8677	0.8487	0.8435	0.8611	0.8487	0.1389
0.9812	23.0	874	0.9465	0.8795	0.8553	0.8497	0.8678	0.8553	0.1322
0.9636	24.0	912	0.9331	0.8820	0.8553	0.8485	0.8699	0.8553	0.1301
0.9591	25.0	950	0.9221	0.8780	0.8618	0.8574	0.8744	0.8618	0.1256
0.948	26.0	988	0.9158	0.8780	0.8618	0.8574	0.8744	0.8684	0.1256
0.9384	27.0	1026	0.9017	0.8685	0.8487	0.8431	0.8601	0.8487	0.1399

Framework versions

• Transformers 4.45.2
• Pytorch 2.5.0+cu121
• Datasets 3.0.1
• Tokenizers 0.20.1