convnextv2-tiny-22k-384-finetuned-galaxy10-decals

This model is a fine-tuned version of facebook/convnextv2-tiny-22k-384 on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:

• Loss: 0.4641
• Accuracy: 0.8675
• Precision: 0.8664
• Recall: 0.8675
• F1: 0.8661

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
1.5875	0.99	62	1.3967	0.5423	0.5237	0.5423	0.5001
0.8561	2.0	125	0.7084	0.7773	0.7769	0.7773	0.7692
0.7139	2.99	187	0.5607	0.8230	0.8201	0.8230	0.8148
0.5799	4.0	250	0.4982	0.8410	0.8428	0.8410	0.8324
0.5352	4.99	312	0.4781	0.8461	0.8470	0.8461	0.8446
0.539	6.0	375	0.4538	0.8523	0.8578	0.8523	0.8482
0.5129	6.99	437	0.4496	0.8472	0.8486	0.8472	0.8468
0.4685	8.0	500	0.4458	0.8551	0.8589	0.8551	0.8542
0.4675	8.99	562	0.4352	0.8613	0.8651	0.8613	0.8579
0.441	10.0	625	0.4076	0.8636	0.8616	0.8636	0.8607
0.4214	10.99	687	0.4346	0.8517	0.8556	0.8517	0.8522
0.4016	12.0	750	0.4300	0.8591	0.8597	0.8591	0.8573
0.3913	12.99	812	0.4164	0.8625	0.8624	0.8625	0.8601
0.3882	14.0	875	0.4246	0.8591	0.8618	0.8591	0.8570
0.3341	14.99	937	0.4321	0.8574	0.8555	0.8574	0.8555
0.3522	16.0	1000	0.4322	0.8568	0.8561	0.8568	0.8542
0.2824	16.99	1062	0.4364	0.8608	0.8606	0.8608	0.8586
0.315	18.0	1125	0.4495	0.8579	0.8581	0.8579	0.8559
0.3084	18.99	1187	0.4536	0.8608	0.8593	0.8608	0.8590
0.2864	20.0	1250	0.4417	0.8630	0.8621	0.8630	0.8607
0.2654	20.99	1312	0.4585	0.8630	0.8628	0.8630	0.8610
0.3067	22.0	1375	0.4673	0.8557	0.8562	0.8557	0.8538
0.2771	22.99	1437	0.4679	0.8596	0.8577	0.8596	0.8577
0.2588	24.0	1500	0.4616	0.8653	0.8646	0.8653	0.8633
0.2583	24.99	1562	0.4726	0.8591	0.8572	0.8591	0.8567
0.2517	26.0	1625	0.4618	0.8630	0.8625	0.8630	0.8619
0.2454	26.99	1687	0.4612	0.8641	0.8630	0.8641	0.8629
0.259	28.0	1750	0.4685	0.8613	0.8595	0.8613	0.8594
0.2388	28.99	1812	0.4668	0.8636	0.8622	0.8636	0.8620
0.2414	29.76	1860	0.4641	0.8675	0.8664	0.8675	0.8661

Framework versions

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