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

Loss: 0.3980
Accuracy: 0.9362

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	Validation Loss	Accuracy
1.9379	0.5	64	1.5473	0.5336
1.0179	1.0	128	0.5529	0.8255
0.4389	1.5	192	0.3367	0.8792
0.2443	2.0	256	0.2354	0.9228
0.1151	2.5	320	0.2422	0.9329
0.0648	3.0	384	0.2351	0.9228
0.0325	3.5	448	0.3316	0.9094
0.0235	4.0	512	0.2389	0.9329
0.0148	4.5	576	0.2317	0.9362
0.0081	5.0	640	0.2005	0.9362
0.0054	5.5	704	0.2198	0.9396
0.0043	6.0	768	0.2171	0.9430
0.0038	6.5	832	0.2200	0.9396
0.003	7.0	896	0.2383	0.9362
0.0183	7.5	960	0.2142	0.9262
0.0126	8.0	1024	0.2931	0.9295
0.0093	8.5	1088	0.2718	0.9396
0.0035	9.0	1152	0.3975	0.9195
0.0019	9.5	1216	0.2839	0.9295
0.0015	10.0	1280	0.2764	0.9329
0.001	10.5	1344	0.3293	0.9195
0.0013	11.0	1408	0.3007	0.9295
0.0011	11.5	1472	0.3193	0.9362
0.0122	12.0	1536	0.2385	0.9463
0.0269	12.5	1600	0.3694	0.9295
0.0092	13.0	1664	0.3212	0.9295
0.0032	13.5	1728	0.2687	0.9362
0.0023	14.0	1792	0.4258	0.9128
0.0015	14.5	1856	0.3478	0.9329
0.0007	15.0	1920	0.3445	0.9329
0.0005	15.5	1984	0.3371	0.9329
0.001	16.0	2048	0.3407	0.9329
0.0079	16.5	2112	0.4630	0.9195
0.0098	17.0	2176	0.3260	0.9362
0.016	17.5	2240	0.5011	0.9195
0.0085	18.0	2304	0.3927	0.9362
0.0057	18.5	2368	0.3910	0.9295
0.0016	19.0	2432	0.3433	0.9262
0.001	19.5	2496	0.4334	0.9228
0.0007	20.0	2560	0.4074	0.9228
0.0005	20.5	2624	0.4064	0.9228
0.0018	21.0	2688	0.5353	0.9228
0.0097	21.5	2752	0.4666	0.9128
0.0127	22.0	2816	0.3886	0.9228
0.0036	22.5	2880	0.2585	0.9430
0.0036	23.0	2944	0.4433	0.9329
0.001	23.5	3008	0.4032	0.9329
0.0006	24.0	3072	0.4428	0.9295
0.0012	24.5	3136	0.4001	0.9228
0.0007	25.0	3200	0.3978	0.9262
0.0006	25.5	3264	0.5251	0.9161
0.0054	26.0	3328	0.5131	0.9228
0.005	26.5	3392	0.4757	0.9027
0.004	27.0	3456	0.4730	0.9195
0.0025	27.5	3520	0.4133	0.9228
0.0012	28.0	3584	0.4215	0.9228
0.0007	28.5	3648	0.4058	0.9329
0.0004	29.0	3712	0.4024	0.9262
0.0003	29.5	3776	0.3974	0.9362
0.0002	30.0	3840	0.3980	0.9362

Framework versions

Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.3.2
Tokenizers 0.21.1

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

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