convnextv2-nano-22k-224-finetuned-galaxy10-decals

This model is a fine-tuned version of facebook/convnextv2-nano-22k-224 on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:

• Loss: 0.4386
• Accuracy: 0.8687
• Precision: 0.8680
• Recall: 0.8687
• F1: 0.8662

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
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.7173	0.99	62	1.5497	0.4617	0.4305	0.4617	0.4119
0.9692	2.0	125	0.8180	0.7306	0.7295	0.7306	0.7246
0.7643	2.99	187	0.6075	0.7931	0.7921	0.7931	0.7880
0.6282	4.0	250	0.5506	0.8151	0.8112	0.8151	0.8068
0.5712	4.99	312	0.5769	0.7982	0.8129	0.7982	0.8002
0.5702	6.0	375	0.5195	0.8315	0.8351	0.8315	0.8225
0.5423	6.99	437	0.4890	0.8331	0.8296	0.8331	0.8303
0.4989	8.0	500	0.4764	0.8371	0.8361	0.8371	0.8342
0.4997	8.99	562	0.4725	0.8405	0.8393	0.8405	0.8365
0.476	10.0	625	0.4582	0.8467	0.8465	0.8467	0.8435
0.4603	10.99	687	0.4460	0.8489	0.8464	0.8489	0.8472
0.4318	12.0	750	0.4398	0.8534	0.8519	0.8534	0.8515
0.4387	12.99	812	0.4575	0.8613	0.8598	0.8613	0.8577
0.4357	14.0	875	0.4398	0.8568	0.8541	0.8568	0.8532
0.3944	14.99	937	0.4425	0.8540	0.8533	0.8540	0.8524
0.3961	16.0	1000	0.4394	0.8574	0.8555	0.8574	0.8542
0.3557	16.99	1062	0.4510	0.8523	0.8497	0.8523	0.8481
0.3881	18.0	1125	0.4399	0.8591	0.8590	0.8591	0.8577
0.3663	18.99	1187	0.4631	0.8546	0.8545	0.8546	0.8524
0.3691	20.0	1250	0.4439	0.8608	0.8585	0.8608	0.8577
0.3443	20.99	1312	0.4524	0.8568	0.8555	0.8568	0.8545
0.3728	22.0	1375	0.4386	0.8687	0.8680	0.8687	0.8662
0.3309	22.99	1437	0.4506	0.8585	0.8578	0.8585	0.8573
0.33	24.0	1500	0.4426	0.8630	0.8613	0.8630	0.8618
0.3541	24.99	1562	0.4625	0.8585	0.8561	0.8585	0.8560
0.2968	26.0	1625	0.4460	0.8613	0.8593	0.8613	0.8590
0.3031	26.99	1687	0.4492	0.8641	0.8630	0.8641	0.8628
0.3207	28.0	1750	0.4480	0.8664	0.8640	0.8664	0.8637
0.2949	28.99	1812	0.4478	0.8636	0.8614	0.8636	0.8615
0.2985	29.76	1860	0.4477	0.8636	0.8612	0.8636	0.8614

Framework versions

• Transformers 4.37.2
• Pytorch 2.3.0
• Datasets 2.19.1
• Tokenizers 0.15.1