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: 50
mixed_precision_training: Native AMP
label_smoothing_factor: 0.15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Recall
2.7724	1.0	20	2.7774	0.2548	0.2320	0.2540
2.7497	2.0	40	2.7553	0.2611	0.2466	0.2559
2.7167	3.0	60	2.7306	0.3949	0.3314	0.3494
2.6803	4.0	80	2.6936	0.4204	0.3092	0.3529
2.6082	5.0	100	2.6261	0.4522	0.3512	0.3889
2.5054	6.0	120	2.5294	0.5796	0.5207	0.5436
2.3585	7.0	140	2.3384	0.6688	0.6220	0.6335
2.1587	8.0	160	2.1049	0.7070	0.6620	0.6738
1.9580	9.0	180	1.8537	0.7516	0.7460	0.7365
1.7723	10.0	200	1.6781	0.8025	0.8037	0.8048
1.6324	11.0	220	1.5635	0.8153	0.8196	0.8219
1.4625	12.0	240	1.4780	0.8790	0.8827	0.8929
1.3762	13.0	260	1.4148	0.8981	0.9011	0.9088
1.3718	14.0	280	1.3859	0.9045	0.9062	0.9161
1.3044	15.0	300	1.3276	0.9108	0.9120	0.9210
1.3963	16.0	320	1.2885	0.9172	0.9177	0.9259
1.2558	17.0	340	1.2743	0.9172	0.9177	0.9259
1.2366	18.0	360	1.2801	0.9236	0.9235	0.9308
1.2488	19.0	380	1.2526	0.9236	0.9248	0.9339
1.2161	20.0	400	1.2495	0.9299	0.9306	0.9388
1.2081	21.0	420	1.2563	0.9172	0.9180	0.9234
1.2564	22.0	440	1.2920	0.9172	0.9190	0.9271
1.1778	23.0	460	1.2459	0.9363	0.9373	0.9462
1.1535	24.0	480	1.2410	0.9299	0.9318	0.9400
1.2084	25.0	500	1.2527	0.9172	0.9173	0.9234
1.1604	26.0	520	1.2605	0.9299	0.9312	0.9369
1.1478	27.0	540	1.2506	0.9299	0.9305	0.9369
1.1828	28.0	560	1.2584	0.9299	0.9306	0.9388
1.1556	29.0	580	1.2603	0.9299	0.9305	0.9369
1.1251	30.0	600	1.2456	0.9299	0.9305	0.9369
1.1211	31.0	620	1.2356	0.9427	0.9448	0.9510
1.1189	32.0	640	1.2556	0.9299	0.9313	0.9374
1.1509	33.0	660	1.2520	0.9363	0.9385	0.9474
1.1181	34.0	680	1.2658	0.9363	0.9385	0.9474
1.1283	35.0	700	1.2746	0.9363	0.9385	0.9474
1.1417	36.0	720	1.2604	0.9363	0.9385	0.9474
1.1355	37.0	740	1.2647	0.9363	0.9385	0.9474
1.0898	38.0	760	1.2626	0.9299	0.9329	0.9393
1.1613	39.0	780	1.2882	0.9236	0.9269	0.9356
1.1201	40.0	800	1.2573	0.9363	0.9385	0.9474
1.1125	41.0	820	1.2405	0.9363	0.9385	0.9454

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

nqvii/resnet50-rice-disease

resnet50-rice-disease

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nqvii/
resnet50-rice-disease