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

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

• Loss: 0.3993
• Accuracy: 0.8732
• Precision: 0.8714
• Recall: 0.8732
• F1: 0.8715

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
1.8139	0.99	62	1.6803	0.4628	0.4589	0.4628	0.3836
1.0894	2.0	125	0.9304	0.6984	0.6965	0.6984	0.6800
0.8423	2.99	187	0.6630	0.7880	0.7858	0.7880	0.7826
0.6564	4.0	250	0.5769	0.8055	0.8091	0.8055	0.7970
0.5927	4.99	312	0.5283	0.8241	0.8276	0.8241	0.8240
0.5853	6.0	375	0.5106	0.8303	0.8342	0.8303	0.8237
0.5757	6.99	437	0.4490	0.8540	0.8514	0.8540	0.8521
0.5235	8.0	500	0.4651	0.8546	0.8578	0.8546	0.8536
0.5166	8.99	562	0.4501	0.8563	0.8551	0.8563	0.8523
0.486	10.0	625	0.4352	0.8647	0.8624	0.8647	0.8626
0.4882	10.99	687	0.4296	0.8613	0.8594	0.8613	0.8597
0.4426	12.0	750	0.4314	0.8579	0.8614	0.8579	0.8566
0.457	12.99	812	0.4226	0.8641	0.8642	0.8641	0.8624
0.4512	14.0	875	0.4319	0.8619	0.8653	0.8619	0.8591
0.4059	14.99	937	0.4124	0.8692	0.8675	0.8692	0.8681
0.4147	16.0	1000	0.3993	0.8732	0.8714	0.8732	0.8715
0.3721	16.99	1062	0.4116	0.8636	0.8609	0.8636	0.8604
0.3908	18.0	1125	0.4098	0.8675	0.8663	0.8675	0.8665
0.3836	18.99	1187	0.4188	0.8670	0.8651	0.8670	0.8651
0.3716	20.0	1250	0.4172	0.8681	0.8653	0.8681	0.8661
0.3484	20.99	1312	0.4404	0.8653	0.8649	0.8653	0.8628
0.3895	22.0	1375	0.4194	0.8698	0.8689	0.8698	0.8688
0.3452	22.99	1437	0.4447	0.8630	0.8634	0.8630	0.8621
0.341	24.0	1500	0.4253	0.8720	0.8722	0.8720	0.8712
0.3481	24.99	1562	0.4325	0.8681	0.8656	0.8681	0.8658
0.3115	26.0	1625	0.4340	0.8619	0.8609	0.8619	0.8603
0.313	26.99	1687	0.4329	0.8653	0.8644	0.8653	0.8644
0.3362	28.0	1750	0.4329	0.8653	0.8636	0.8653	0.8639
0.3056	28.99	1812	0.4342	0.8658	0.8645	0.8658	0.8644
0.3206	29.76	1860	0.4343	0.8664	0.8648	0.8664	0.8649

Framework versions

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