resnet50_fold_2

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: 1.2330
• Accuracy: 0.9276
• F1 Score: 0.9327
• Recall: 0.9355

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
2.7704	1.0	19	2.7578	0.4	0.2936	0.3646
2.7541	2.0	38	2.7434	0.4069	0.2909	0.3420
2.7183	3.0	57	2.7200	0.4034	0.2708	0.3223
2.6750	4.0	76	2.6838	0.4103	0.2821	0.3313
2.6012	5.0	95	2.6482	0.4414	0.3326	0.3737
2.4884	6.0	114	2.5733	0.5276	0.4471	0.4836
2.3305	7.0	133	2.4639	0.6	0.5363	0.5778
2.1833	8.0	152	2.2702	0.7241	0.6861	0.7149
1.9297	9.0	171	1.9626	0.8	0.8048	0.8041
1.7430	10.0	190	1.6955	0.8483	0.8528	0.8538
1.6346	11.0	209	1.5015	0.8828	0.8922	0.8928
1.4690	12.0	228	1.4260	0.8862	0.8941	0.8979
1.3484	13.0	247	1.3753	0.8897	0.8963	0.8989
1.2518	14.0	266	1.3165	0.9	0.9077	0.9111
1.2253	15.0	285	1.3249	0.9172	0.9235	0.9271
1.2322	16.0	304	1.3016	0.8966	0.9041	0.9075
1.2039	17.0	323	1.3057	0.9034	0.9110	0.9136
1.1545	18.0	342	1.2971	0.9103	0.9163	0.9210
1.1947	19.0	361	1.2571	0.9172	0.9230	0.9258
1.2689	20.0	380	1.2523	0.9034	0.9113	0.9112
1.1614	21.0	399	1.2753	0.9069	0.9147	0.9160
1.1393	22.0	418	1.2788	0.9034	0.9101	0.9110
1.1489	23.0	437	1.2541	0.9207	0.9261	0.9258
1.1225	24.0	456	1.2571	0.9103	0.9175	0.9184
1.1244	25.0	475	1.2627	0.9172	0.9233	0.9245
1.1049	26.0	494	1.2678	0.9207	0.9275	0.9319
1.1145	27.0	513	1.2552	0.9241	0.9293	0.9294
1.1077	28.0	532	1.2575	0.9172	0.9239	0.9259
1.1031	29.0	551	1.2473	0.9172	0.9232	0.9270
1.1690	30.0	570	1.2503	0.9207	0.9256	0.9245
1.0949	31.0	589	1.2366	0.9103	0.9170	0.9161
1.0662	32.0	608	1.2516	0.9103	0.9169	0.9194
1.1033	33.0	627	1.2593	0.9138	0.9205	0.9210
1.1002	34.0	646	1.2317	0.9207	0.9251	0.9257
1.0843	35.0	665	1.2484	0.9138	0.9194	0.9196
1.0412	36.0	684	1.2428	0.9172	0.9236	0.9258
1.0517	37.0	703	1.2349	0.9138	0.9197	0.9209
1.0436	38.0	722	1.2566	0.9069	0.9124	0.9124
1.0631	39.0	741	1.2451	0.9207	0.9268	0.9283
1.0571	40.0	760	1.2434	0.9241	0.9293	0.9294
1.0431	41.0	779	1.2330	0.9276	0.9327	0.9355
1.0494	42.0	798	1.2347	0.9207	0.9256	0.9281
1.0663	43.0	817	1.2327	0.9138	0.9185	0.9196
1.0452	44.0	836	1.2392	0.9241	0.9299	0.9319

Framework versions

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