dinov2-large-finetuned-galaxy10-decals

This model is a fine-tuned version of facebook/dinov2-large on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:

• Loss: 0.5869
• Accuracy: 0.8737
• Precision: 0.8722
• Recall: 0.8737
• F1: 0.8722

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
0.7564	0.99	62	0.6187	0.7976	0.8171	0.7976	0.7990
0.7766	2.0	125	0.6102	0.7852	0.8052	0.7852	0.7782
0.7103	2.99	187	0.5744	0.8089	0.8140	0.8089	0.8032
0.6704	4.0	250	0.6859	0.7745	0.7899	0.7745	0.7663
0.599	4.99	312	0.4729	0.8377	0.8412	0.8377	0.8359
0.565	6.0	375	0.4465	0.8517	0.8542	0.8517	0.8507
0.5576	6.99	437	0.4479	0.8484	0.8565	0.8484	0.8452
0.4966	8.0	500	0.4870	0.8388	0.8399	0.8388	0.8363
0.4667	8.99	562	0.4763	0.8444	0.8496	0.8444	0.8443
0.4264	10.0	625	0.4802	0.8377	0.8378	0.8377	0.8324
0.445	10.99	687	0.5246	0.8377	0.8383	0.8377	0.8343
0.3935	12.0	750	0.4883	0.8439	0.8519	0.8439	0.8434
0.374	12.99	812	0.4511	0.8568	0.8603	0.8568	0.8569
0.3551	14.0	875	0.5153	0.8546	0.8517	0.8546	0.8496
0.3573	14.99	937	0.4705	0.8579	0.8554	0.8579	0.8559
0.3385	16.0	1000	0.4547	0.8517	0.8535	0.8517	0.8517
0.2764	16.99	1062	0.5189	0.8529	0.8544	0.8529	0.8513
0.2895	18.0	1125	0.5393	0.8602	0.8587	0.8602	0.8586
0.2738	18.99	1187	0.5554	0.8405	0.8436	0.8405	0.8381
0.2563	20.0	1250	0.5478	0.8608	0.8573	0.8608	0.8574
0.2375	20.99	1312	0.5512	0.8664	0.8651	0.8664	0.8622
0.2599	22.0	1375	0.5317	0.8625	0.8607	0.8625	0.8599
0.2146	22.99	1437	0.5972	0.8568	0.8567	0.8568	0.8559
0.2132	24.0	1500	0.5934	0.8636	0.8617	0.8636	0.8606
0.2036	24.99	1562	0.5923	0.8664	0.8662	0.8664	0.8658
0.1971	26.0	1625	0.5839	0.8630	0.8621	0.8630	0.8621
0.1878	26.99	1687	0.5907	0.8625	0.8669	0.8625	0.8640
0.1922	28.0	1750	0.6058	0.8692	0.8684	0.8692	0.8680
0.1854	28.99	1812	0.6014	0.8670	0.8653	0.8670	0.8655
0.1688	29.76	1860	0.5869	0.8737	0.8722	0.8737	0.8722

Framework versions

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