resnet50_fold_1

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

• Loss: 1.1010
• Accuracy: 0.9690
• F1 Score: 0.9708
• Recall: 0.9730

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: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 150
• num_epochs: 100
• label_smoothing_factor: 0.15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Recall
2.7705	1.0	19	2.7688	0.2241	0.1833	0.2211
2.7598	2.0	38	2.7554	0.2793	0.1855	0.2301
2.7265	3.0	57	2.7304	0.3483	0.1995	0.2700
2.6927	4.0	76	2.6900	0.3586	0.1698	0.2627
2.6274	5.0	95	2.6449	0.3586	0.1505	0.2583
2.5275	6.0	114	2.5764	0.3931	0.2326	0.3025
2.4048	7.0	133	2.4666	0.5483	0.4814	0.5142
2.2350	8.0	152	2.2408	0.7069	0.6959	0.6968
1.9944	9.0	171	1.9572	0.8103	0.8143	0.8093
1.7608	10.0	190	1.6624	0.8724	0.8746	0.8781
1.6519	11.0	209	1.4591	0.9	0.9074	0.9084
1.5012	12.0	228	1.3566	0.9034	0.9111	0.9144
1.4065	13.0	247	1.3477	0.9034	0.9043	0.9132
1.3178	14.0	266	1.2477	0.9172	0.9231	0.9291
1.2779	15.0	285	1.2359	0.9241	0.9283	0.9329
1.2558	16.0	304	1.2012	0.9379	0.9427	0.9474
1.1987	17.0	323	1.2123	0.9345	0.9353	0.9425
1.1862	18.0	342	1.2075	0.9379	0.9385	0.9462
1.2160	19.0	361	1.1711	0.9379	0.9392	0.9449
1.3062	20.0	380	1.1439	0.9586	0.9585	0.9608
1.1480	21.0	399	1.1638	0.9517	0.9525	0.9596
1.1471	22.0	418	1.1646	0.9517	0.9521	0.9571
1.2000	23.0	437	1.1465	0.9483	0.9496	0.9534
1.1213	24.0	456	1.1368	0.9483	0.9481	0.9522
1.1640	25.0	475	1.1115	0.9621	0.9632	0.9657
1.1094	26.0	494	1.1468	0.9586	0.9606	0.9657
1.1218	27.0	513	1.1325	0.9483	0.9463	0.9522
1.1314	28.0	532	1.1256	0.9483	0.9480	0.9547
1.1161	29.0	551	1.1225	0.9552	0.9560	0.9608
1.1771	30.0	570	1.1137	0.9448	0.9430	0.9485
1.1027	31.0	589	1.1234	0.9448	0.9427	0.9473
1.1052	32.0	608	1.1542	0.9448	0.9428	0.9498
1.1163	33.0	627	1.1121	0.9552	0.9546	0.9571
1.0949	34.0	646	1.1226	0.9517	0.9523	0.9547
1.1121	35.0	665	1.1010	0.9690	0.9708	0.9730
1.0660	36.0	684	1.1164	0.9586	0.9600	0.9581
1.0682	37.0	703	1.1129	0.9655	0.9664	0.9694
1.0752	38.0	722	1.1153	0.9655	0.9637	0.9681
1.0583	39.0	741	1.1141	0.9552	0.9551	0.9596
1.0750	40.0	760	1.1140	0.9621	0.9639	0.9669
1.0351	41.0	779	1.1209	0.9655	0.9657	0.9694
1.0556	42.0	798	1.1155	0.9586	0.9581	0.9620
1.0381	43.0	817	1.1226	0.9483	0.9500	0.9559
1.0185	44.0	836	1.1488	0.9517	0.9537	0.9596
1.0324	45.0	855	1.1278	0.9552	0.9553	0.9608

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 5.0.0
• Tokenizers 0.22.2