efficientnet-b0_rice-leaf-disease-augmented-v4_fft

This model is a fine-tuned version of google/efficientnet-b0 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.3800
• Accuracy: 0.9128

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: 5e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine_with_restarts
• lr_scheduler_warmup_steps: 256
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Accuracy	Validation Loss
2.0658	0.5	64	0.2450	1.9987
1.9318	1.0	128	0.4262	1.7967
1.6451	1.5	192	0.5604	1.4543
1.2851	2.0	256	0.6846	1.0678
0.9017	2.5	320	0.7617	0.8011
0.6891	3.0	384	0.8054	0.6284
0.5118	3.5	448	0.8020	0.6124
0.4458	4.0	512	0.8121	0.6052
0.356	4.5	576	0.8289	0.5032
0.3255	5.0	640	0.8523	0.4429
0.294	5.5	704	0.8490	0.4829
0.2856	6.0	768	0.8389	0.4863
0.289	6.5	832	0.8523	0.4306
0.2357	7.0	896	0.8591	0.4408
0.183	7.5	960	0.8691	0.3810
0.1446	8.0	1024	0.8624	0.3680
0.1094	8.5	1088	0.8826	0.3557
0.102	9.0	1152	0.8826	0.3555
0.0752	9.5	1216	0.8826	0.3514
0.0828	10.0	1280	0.8691	0.4008
0.0758	10.5	1344	0.8792	0.3509
0.0699	11.0	1408	0.8893	0.3737
0.059	11.5	1472	0.8859	0.3488
0.0504	12.0	1536	0.9027	0.3544
0.0381	12.5	1600	0.8926	0.3867
0.0293	13.0	1664	0.8893	0.3593
0.0257	13.5	1728	0.8993	0.3230
0.0258	14.0	1792	0.8893	0.4092
0.0249	14.5	1856	0.8859	0.4291
0.0187	15.0	1920	0.8993	0.3305
0.0226	15.5	1984	0.3599	0.8893
0.0222	16.0	2048	0.3777	0.8993
0.0151	16.5	2112	0.3345	0.9060
0.0181	17.0	2176	0.3589	0.8960
0.0144	17.5	2240	0.3440	0.8960
0.0117	18.0	2304	0.4319	0.9027
0.013	18.5	2368	0.3972	0.8960
0.0111	19.0	2432	0.3337	0.8926
0.0106	19.5	2496	0.3284	0.9027
0.0114	20.0	2560	0.3483	0.9027
0.0111	20.5	2624	0.3425	0.8993
0.0106	21.0	2688	0.3333	0.8993
0.011	21.5	2752	0.3795	0.9027
0.0106	22.0	2816	0.4228	0.9027
0.0075	22.5	2880	0.4410	0.8859
0.0075	23.0	2944	0.3461	0.8993
0.0057	23.5	3008	0.3992	0.9027
0.0058	24.0	3072	0.3577	0.9060
0.0041	24.5	3136	0.4393	0.8993
0.0042	25.0	3200	0.3816	0.8960
0.0045	25.5	3264	0.4059	0.9027
0.0033	26.0	3328	0.4299	0.8960
0.0038	26.5	3392	0.4605	0.8993
0.004	27.0	3456	0.3973	0.9027
0.0033	27.5	3520	0.4126	0.9094
0.0049	28.0	3584	0.3783	0.9128
0.0027	28.5	3648	0.3632	0.9094
0.0033	29.0	3712	0.4586	0.9027
0.0039	29.5	3776	0.4505	0.8960
0.0037	30.0	3840	0.3800	0.9128

Framework versions

• Transformers 4.48.3
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.1