convnextv2-base-1k-224_rice-leaf-disease-augmented-v4_v5_pft

This model is a fine-tuned version of facebook/convnextv2-base-1k-224 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6774
• Accuracy: 0.7819

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
2.0691	0.5	64	2.0083	0.3523
1.9566	1.0	128	1.8732	0.5201
1.7715	1.5	192	1.6903	0.5638
1.5752	2.0	256	1.5040	0.6074
1.4088	2.5	320	1.3569	0.6208
1.2927	3.0	384	1.2600	0.6309
1.2173	3.5	448	1.1948	0.6577
1.1515	4.0	512	1.1464	0.6644
1.1183	4.5	576	1.1160	0.6711
1.0893	5.0	640	1.1001	0.6879
1.0792	5.5	704	1.0898	0.6913
1.0627	6.0	768	1.0831	0.6846
1.0714	6.5	832	1.0817	0.6846
1.0459	7.0	896	1.0483	0.6913
1.0282	7.5	960	1.0047	0.6980
0.9605	8.0	1024	0.9774	0.7081
0.9405	8.5	1088	0.9489	0.7114
0.9316	9.0	1152	0.9353	0.7148
0.9174	9.5	1216	0.9208	0.7181
0.8924	10.0	1280	0.9137	0.7215
0.9009	10.5	1344	0.9101	0.7282
0.8844	11.0	1408	0.9092	0.7248
0.8873	11.5	1472	0.9076	0.7215
0.8751	12.0	1536	0.8721	0.7383
0.8553	12.5	1600	0.8617	0.7248
0.8265	13.0	1664	0.8428	0.7416
0.8133	13.5	1728	0.8302	0.7416
0.808	14.0	1792	0.8232	0.7483
0.7915	14.5	1856	0.8187	0.7450
0.7975	15.0	1920	0.8157	0.7450
0.7765	15.5	1984	0.8143	0.7450
0.8017	16.0	2048	0.8142	0.7450
0.793	16.5	2112	0.7970	0.7584
0.7567	17.0	2176	0.7901	0.7550
0.7576	17.5	2240	0.7785	0.7483
0.7377	18.0	2304	0.7651	0.7651
0.7311	18.5	2368	0.7588	0.7651
0.7276	19.0	2432	0.7566	0.7651
0.7237	19.5	2496	0.7567	0.7651
0.7171	20.0	2560	0.7534	0.7685
0.7158	20.5	2624	0.7529	0.7685
0.7188	21.0	2688	0.7486	0.7651
0.7112	21.5	2752	0.7340	0.7752
0.6912	22.0	2816	0.7297	0.7752
0.6784	22.5	2880	0.7229	0.7785
0.6868	23.0	2944	0.7152	0.7752
0.6701	23.5	3008	0.7132	0.7819
0.6718	24.0	3072	0.7111	0.7752
0.671	24.5	3136	0.7105	0.7785
0.6609	25.0	3200	0.7097	0.7785
0.6722	25.5	3264	0.7066	0.7785
0.6526	26.0	3328	0.6959	0.7785
0.6448	26.5	3392	0.6920	0.7919
0.6493	27.0	3456	0.6903	0.7785
0.6394	27.5	3520	0.6816	0.7785
0.6274	28.0	3584	0.6819	0.7819
0.6198	28.5	3648	0.6784	0.7819
0.632	29.0	3712	0.6778	0.7819
0.634	29.5	3776	0.6776	0.7819
0.612	30.0	3840	0.6774	0.7819

Framework versions

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