This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6225
Accuracy: 0.9262

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
1.9171	0.5	64	0.5403	1.4134
0.8021	1.0	128	0.8725	0.3672
0.2453	1.5	192	0.8624	0.3582
0.1156	2.0	256	0.9463	0.2018
0.0502	2.5	320	0.9262	0.2554
0.0396	3.0	384	0.9262	0.3171
0.0241	3.5	448	0.9295	0.2359
0.0106	4.0	512	0.9228	0.3841
0.003	4.5	576	0.9396	0.3358
0.0028	5.0	640	0.9396	0.2688
0.0008	5.5	704	0.9362	0.3238
0.0003	6.0	768	0.9329	0.3211
0.0004	6.5	832	0.9362	0.3208
0.0012	7.0	896	0.9430	0.3044
0.0191	7.5	960	0.9161	0.5238
0.0093	8.0	1024	0.9161	0.5086
0.0029	8.5	1088	0.9396	0.3395
0.0067	9.0	1152	0.9329	0.3446
0.0013	9.5	1216	0.9329	0.3731
0.0014	10.0	1280	0.9295	0.3921
0.0018	10.5	1344	0.9362	0.3714
0.0002	11.0	1408	0.9295	0.3655
0.001	11.5	1472	0.9362	0.4734
0.0035	12.0	1536	0.9463	0.3671
0.0183	12.5	1600	0.9295	0.4800
0.0148	13.0	1664	0.9262	0.5419
0.0067	13.5	1728	0.9362	0.5487
0.0029	14.0	1792	0.9362	0.5559
0.0008	14.5	1856	0.9396	0.5968
0.0008	15.0	1920	0.9463	0.4915
0.0016	15.5	1984	0.9430	0.5009
0.0001	16.0	2048	0.9430	0.5010
0.0005	16.5	2112	0.9430	0.4041
0.0042	17.0	2176	0.9463	0.5723
0.0019	17.5	2240	0.9262	0.6179
0.0031	18.0	2304	0.9463	0.4994
0.002	18.5	2368	0.9329	0.6108
0.0009	19.0	2432	0.9430	0.5313
0.0001	19.5	2496	0.9329	0.5379
0.0	20.0	2560	0.9362	0.5363
0.0002	20.5	2624	0.9362	0.5345
0.0001	21.0	2688	0.9430	0.5184
0.0035	21.5	2752	0.9195	0.6128
0.013	22.0	2816	0.9161	0.6121
0.0184	22.5	2880	0.9295	0.5990
0.0074	23.0	2944	0.9329	0.5262
0.0024	23.5	3008	0.9329	0.5637
0.0002	24.0	3072	0.9497	0.4270
0.0001	24.5	3136	0.9396	0.4547
0.0001	25.0	3200	0.9396	0.4528
0.0	25.5	3264	0.9396	0.4483
0.0001	26.0	3328	0.9396	0.4772
0.0018	26.5	3392	0.9430	0.4525
0.0119	27.0	3456	0.8893	0.7561
0.0052	27.5	3520	0.9329	0.6145
0.0007	28.0	3584	0.9228	0.7502
0.0002	28.5	3648	0.9228	0.6163
0.0004	29.0	3712	0.9262	0.6056
0.0002	29.5	3776	0.9262	0.6198
0.0002	30.0	3840	0.6225	0.9262

Framework versions

Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.3.2
Tokenizers 0.21.1

SodaXII/swin-base-patch4-window7-224_rice-leaf-disease-augmented-v4_v5_fft

swin-base-patch4-window7-224_rice-leaf-disease-augmented-v4_v5_fft

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

SodaXII/
swin-base-patch4-window7-224_rice-leaf-disease-augmented-v4_v5_fft