efficientnet-b2_rice-leaf-disease-augmented-v4_v5_fft

This model is a fine-tuned version of google/efficientnet-b2 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2236
• 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
2.0775	0.5	64	2.0200	0.2282
1.9215	1.0	128	1.7530	0.5336
1.5941	1.5	192	1.3576	0.6309
1.1312	2.0	256	0.8490	0.7517
0.6799	2.5	320	0.5743	0.8221
0.4743	3.0	384	0.4281	0.8624
0.2937	3.5	448	0.3946	0.8893
0.2342	4.0	512	0.3713	0.8758
0.1563	4.5	576	0.3339	0.8893
0.1296	5.0	640	0.2886	0.9128
0.1026	5.5	704	0.3032	0.8926
0.1009	6.0	768	0.2951	0.8893
0.0956	6.5	832	0.2795	0.9128
0.0817	7.0	896	0.3031	0.9094
0.0591	7.5	960	0.2778	0.9195
0.0444	8.0	1024	0.2435	0.9060
0.0268	8.5	1088	0.2506	0.9228
0.0198	9.0	1152	0.2692	0.8993
0.0139	9.5	1216	0.2384	0.9195
0.0164	10.0	1280	0.2712	0.9195
0.0118	10.5	1344	0.2868	0.9128
0.0139	11.0	1408	0.2262	0.9295
0.0122	11.5	1472	0.2492	0.9128
0.0132	12.0	1536	0.2751	0.9128
0.0084	12.5	1600	0.3184	0.8993
0.0082	13.0	1664	0.2596	0.9228
0.0093	13.5	1728	0.2636	0.9228
0.0059	14.0	1792	0.2501	0.9262
0.0060	14.5	1856	0.3249	0.9027
0.0036	15.0	1920	0.2584	0.9228
0.0051	15.5	1984	0.2501	0.9161
0.0049	16.0	2048	0.2698	0.9228
0.0042	16.5	2112	0.2403	0.9262
0.0054	17.0	2176	0.2536	0.9262
0.0056	17.5	2240	0.2506	0.9228
0.0031	18.0	2304	0.3199	0.9027
0.0038	18.5	2368	0.3303	0.9228
0.0029	19.0	2432	0.2250	0.9295
0.0024	19.5	2496	0.2577	0.9161
0.0023	20.0	2560	0.2365	0.9396
0.0026	20.5	2624	0.2501	0.9295
0.0034	21.0	2688	0.2283	0.9262
0.0034	21.5	2752	0.2608	0.9195
0.0045	22.0	2816	0.3040	0.9094
0.0023	22.5	2880	0.2782	0.9228
0.0026	23.0	2944	0.2520	0.9161
0.0015	23.5	3008	0.2440	0.9228
0.0015	24.0	3072	0.2341	0.9362
0.0019	24.5	3136	0.2779	0.9161
0.0018	25.0	3200	0.2662	0.9362
0.0016	25.5	3264	0.2244	0.9362
0.0014	26.0	3328	0.3105	0.9161
0.0016	26.5	3392	0.2696	0.9195
0.0022	27.0	3456	0.2696	0.9262
0.0012	27.5	3520	0.2382	0.9362
0.0019	28.0	3584	0.2430	0.9329
0.0010	28.5	3648	0.2321	0.9396
0.0010	29.0	3712	0.2566	0.9161
0.0017	29.5	3776	0.2965	0.9295
0.0013	30.0	3840	0.2236	0.9362

Framework versions

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