resnet50_fold_3

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: 1.1685
• Accuracy: 0.9517
• F1 Score: 0.9556
• Recall: 0.9609

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
2.7675	1.0	19	2.7627	0.2276	0.1794	0.2296
2.7523	2.0	38	2.7540	0.3241	0.2311	0.2797
2.7348	3.0	57	2.7263	0.3828	0.2281	0.2988
2.6936	4.0	76	2.6837	0.3793	0.2013	0.2840
2.6260	5.0	95	2.6374	0.3828	0.1989	0.2850
2.5096	6.0	114	2.5647	0.4276	0.2991	0.3443
2.3841	7.0	133	2.4568	0.5966	0.5553	0.5545
2.2120	8.0	152	2.2170	0.7172	0.7022	0.6993
1.9531	9.0	171	1.9750	0.8	0.8021	0.8009
1.8001	10.0	190	1.7220	0.8310	0.8415	0.8368
1.6636	11.0	209	1.5142	0.8759	0.8851	0.8898
1.4820	12.0	228	1.3768	0.8828	0.8913	0.8960
1.3627	13.0	247	1.3274	0.8966	0.9016	0.9069
1.2971	14.0	266	1.2548	0.9345	0.9376	0.9411
1.2295	15.0	285	1.2514	0.9276	0.9347	0.9391
1.2444	16.0	304	1.2267	0.9345	0.9406	0.9427
1.2220	17.0	323	1.2259	0.9345	0.9369	0.9397
1.1872	18.0	342	1.2203	0.9241	0.9260	0.9349
1.2176	19.0	361	1.1870	0.9310	0.9358	0.9411
1.2911	20.0	380	1.1895	0.9414	0.9461	0.9446
1.1779	21.0	399	1.1768	0.9448	0.9488	0.9534
1.1306	22.0	418	1.1870	0.9345	0.9395	0.9371
1.1647	23.0	437	1.1661	0.9448	0.9502	0.9547
1.1289	24.0	456	1.1722	0.9345	0.9402	0.9448
1.1109	25.0	475	1.1681	0.9448	0.9499	0.9521
1.1047	26.0	494	1.1909	0.9345	0.9401	0.9473
1.1087	27.0	513	1.1579	0.9483	0.9523	0.9533
1.1312	28.0	532	1.1588	0.9448	0.9487	0.9521
1.1221	29.0	551	1.1549	0.9483	0.9538	0.9572
1.1603	30.0	570	1.1495	0.9448	0.9498	0.9534
1.1105	31.0	589	1.1553	0.9379	0.9443	0.9448
1.1005	32.0	608	1.1685	0.9517	0.9556	0.9609
1.1101	33.0	627	1.1662	0.9414	0.9466	0.9484
1.0823	34.0	646	1.1724	0.9345	0.9403	0.9409
1.1197	35.0	665	1.1616	0.9448	0.9499	0.9521
1.0557	36.0	684	1.1553	0.9379	0.9433	0.9434
1.0602	37.0	703	1.1578	0.9414	0.9463	0.9484
1.0646	38.0	722	1.1797	0.9276	0.9316	0.9348
1.0431	39.0	741	1.1758	0.9483	0.9526	0.9559
1.0977	40.0	760	1.1542	0.9414	0.9460	0.9471

Framework versions

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