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

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

• Loss: 0.4373
• Accuracy: 0.8636
• Precision: 0.8625
• Recall: 0.8636
• F1: 0.8603

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
1.5665	0.99	62	1.3996	0.5287	0.5180	0.5287	0.4897
0.8598	2.0	125	0.7433	0.7463	0.7490	0.7463	0.7396
0.7163	2.99	187	0.5703	0.7948	0.7919	0.7948	0.7863
0.5858	4.0	250	0.5194	0.8269	0.8292	0.8269	0.8190
0.5382	4.99	312	0.4936	0.8309	0.8314	0.8309	0.8302
0.5546	6.0	375	0.5054	0.8292	0.8366	0.8292	0.8234
0.5067	6.99	437	0.4817	0.8281	0.8324	0.8281	0.8278
0.4617	8.0	500	0.4565	0.8501	0.8545	0.8501	0.8497
0.4619	8.99	562	0.4382	0.8534	0.8520	0.8534	0.8498
0.4416	10.0	625	0.4330	0.8529	0.8505	0.8529	0.8504
0.4267	10.99	687	0.4274	0.8574	0.8575	0.8574	0.8566
0.3919	12.0	750	0.4407	0.8585	0.8604	0.8585	0.8563
0.3929	12.99	812	0.4373	0.8636	0.8625	0.8636	0.8603
0.3989	14.0	875	0.4351	0.8585	0.8602	0.8585	0.8577
0.3426	14.99	937	0.4476	0.8495	0.8500	0.8495	0.8484
0.361	16.0	1000	0.4463	0.8517	0.8505	0.8517	0.8501
0.2996	16.99	1062	0.4694	0.8596	0.8604	0.8596	0.8579
0.3394	18.0	1125	0.4494	0.8523	0.8526	0.8523	0.8517
0.3207	18.99	1187	0.4863	0.8506	0.8502	0.8506	0.8496
0.2993	20.0	1250	0.4748	0.8551	0.8516	0.8551	0.8521
0.287	20.99	1312	0.4980	0.8467	0.8436	0.8467	0.8434
0.3331	22.0	1375	0.4829	0.8546	0.8530	0.8546	0.8519
0.2852	22.99	1437	0.4943	0.8512	0.8520	0.8512	0.8508
0.2813	24.0	1500	0.4796	0.8574	0.8574	0.8574	0.8568
0.2807	24.99	1562	0.4811	0.8596	0.8576	0.8596	0.8576
0.2609	26.0	1625	0.4786	0.8608	0.8589	0.8608	0.8592
0.2571	26.99	1687	0.4777	0.8608	0.8605	0.8608	0.8602
0.2807	28.0	1750	0.4879	0.8596	0.8580	0.8596	0.8582
0.2578	28.99	1812	0.4829	0.8557	0.8550	0.8557	0.8549
0.2543	29.76	1860	0.4833	0.8563	0.8555	0.8563	0.8554

Framework versions

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