dinov2-base_rice-leaf-disease-augmented-v4_v5_pft

This model is a fine-tuned version of facebook/dinov2-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2563
• Accuracy: 0.9295

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: 0.0003
• 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
2.283	0.5	64	1.9783	0.2013
1.4862	1.0	128	1.1465	0.6745
0.9264	1.5	192	0.7663	0.7550
0.6628	2.0	256	0.6171	0.8020
0.5289	2.5	320	0.5400	0.8322
0.4616	3.0	384	0.5054	0.8322
0.4022	3.5	448	0.4499	0.8490
0.3653	4.0	512	0.4136	0.8691
0.3432	4.5	576	0.4172	0.8691
0.3168	5.0	640	0.3760	0.8826
0.3124	5.5	704	0.3938	0.8658
0.2992	6.0	768	0.3811	0.8859
0.3062	6.5	832	0.3866	0.8826
0.2951	7.0	896	0.3926	0.8725
0.2967	7.5	960	0.3657	0.8758
0.2635	8.0	1024	0.3501	0.8859
0.2474	8.5	1088	0.3510	0.8993
0.2452	9.0	1152	0.3402	0.8960
0.2185	9.5	1216	0.3353	0.8926
0.2283	10.0	1280	0.3332	0.9060
0.2138	10.5	1344	0.3235	0.9027
0.2143	11.0	1408	0.3267	0.8993
0.2156	11.5	1472	0.3379	0.8926
0.2125	12.0	1536	0.3256	0.8826
0.1998	12.5	1600	0.3135	0.8926
0.1961	13.0	1664	0.3031	0.8993
0.1818	13.5	1728	0.2855	0.9228
0.1788	14.0	1792	0.2977	0.9094
0.1709	14.5	1856	0.3101	0.9060
0.1652	15.0	1920	0.2969	0.9128
0.1578	15.5	1984	0.2916	0.9161
0.1661	16.0	2048	0.2904	0.9195
0.1727	16.5	2112	0.2854	0.9128
0.1617	17.0	2176	0.2802	0.9128
0.1519	17.5	2240	0.2954	0.8993
0.1491	18.0	2304	0.2812	0.9094
0.1366	18.5	2368	0.2877	0.9060
0.1424	19.0	2432	0.2791	0.9195
0.1297	19.5	2496	0.2810	0.9161
0.1325	20.0	2560	0.2787	0.9161
0.1309	20.5	2624	0.2764	0.9195
0.1298	21.0	2688	0.2715	0.9161
0.1334	21.5	2752	0.2768	0.9094
0.1268	22.0	2816	0.2712	0.9128
0.1169	22.5	2880	0.2739	0.9195
0.1203	23.0	2944	0.2607	0.9262
0.1091	23.5	3008	0.2703	0.9161
0.1123	24.0	3072	0.2611	0.9262
0.1056	24.5	3136	0.2606	0.9262
0.1061	25.0	3200	0.2630	0.9262
0.112	25.5	3264	0.2899	0.9094
0.1076	26.0	3328	0.2749	0.9195
0.105	26.5	3392	0.2570	0.9329
0.1048	27.0	3456	0.2654	0.9295
0.098	27.5	3520	0.2593	0.9262
0.0951	28.0	3584	0.2591	0.9295
0.0915	28.5	3648	0.2553	0.9262
0.0907	29.0	3712	0.2568	0.9295
0.0885	29.5	3776	0.2582	0.9295
0.087	30.0	3840	0.2563	0.9295

Framework versions

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