convnextv2-large-1k-224-finetuned-galaxy10-decals

This model is a fine-tuned version of facebook/convnextv2-large-1k-224 on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:

• Loss: 0.4479
• Accuracy: 0.8681
• Precision: 0.8670
• Recall: 0.8681
• F1: 0.8668

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
1.9261	0.99	62	1.8153	0.4696	0.5070	0.4696	0.3875
1.2684	2.0	125	1.1432	0.6793	0.6395	0.6793	0.6478
0.9177	2.99	187	0.7477	0.7847	0.7832	0.7847	0.7720
0.6937	4.0	250	0.5962	0.8168	0.8145	0.8168	0.8104
0.5937	4.99	312	0.5862	0.8191	0.8234	0.8191	0.8167
0.5921	6.0	375	0.5389	0.8365	0.8454	0.8365	0.8300
0.557	6.99	437	0.4944	0.8433	0.8478	0.8433	0.8410
0.5522	8.0	500	0.5022	0.8427	0.8508	0.8427	0.8416
0.5028	8.99	562	0.4481	0.8579	0.8610	0.8579	0.8580
0.4801	10.0	625	0.4360	0.8551	0.8536	0.8551	0.8527
0.4475	10.99	687	0.4663	0.8410	0.8423	0.8410	0.8407
0.411	12.0	750	0.4444	0.8546	0.8552	0.8546	0.8538
0.4173	12.99	812	0.4341	0.8613	0.8627	0.8613	0.8595
0.3995	14.0	875	0.4380	0.8653	0.8655	0.8653	0.8637
0.3657	14.99	937	0.4659	0.8625	0.8633	0.8625	0.8615
0.3533	16.0	1000	0.4600	0.8602	0.8592	0.8602	0.8585
0.3001	16.99	1062	0.5069	0.8478	0.8455	0.8478	0.8450
0.318	18.0	1125	0.4647	0.8574	0.8576	0.8574	0.8552
0.3029	18.99	1187	0.4479	0.8681	0.8670	0.8681	0.8668
0.2915	20.0	1250	0.4772	0.8625	0.8598	0.8625	0.8586
0.2742	20.99	1312	0.4798	0.8557	0.8538	0.8557	0.8521
0.3067	22.0	1375	0.4767	0.8602	0.8573	0.8602	0.8575
0.2758	22.99	1437	0.5099	0.8506	0.8547	0.8506	0.8516
0.2527	24.0	1500	0.5016	0.8585	0.8563	0.8585	0.8565
0.253	24.99	1562	0.4990	0.8625	0.8605	0.8625	0.8604
0.2361	26.0	1625	0.4903	0.8602	0.8590	0.8602	0.8591
0.2325	26.99	1687	0.5062	0.8602	0.8612	0.8602	0.8600
0.2448	28.0	1750	0.4997	0.8670	0.8648	0.8670	0.8646
0.2354	28.99	1812	0.4956	0.8608	0.8586	0.8608	0.8590
0.2156	29.76	1860	0.4970	0.8630	0.8615	0.8630	0.8617

Framework versions

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