dinov2-small-finetuned-galaxy10-decals

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

• Loss: 0.4852
• Accuracy: 0.8613
• Precision: 0.8626
• Recall: 0.8613
• F1: 0.8615

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: 128
• eval_batch_size: 128
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 512
• 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
1.279	0.99	31	0.9926	0.6454	0.6589	0.6454	0.6242
0.8187	1.98	62	0.6014	0.7948	0.8151	0.7948	0.7948
0.77	2.98	93	0.6323	0.7818	0.7928	0.7818	0.7777
0.6743	4.0	125	0.5555	0.8140	0.8149	0.8140	0.8083
0.6407	4.99	156	0.5732	0.8078	0.8174	0.8078	0.8096
0.64	5.98	187	0.4912	0.8360	0.8344	0.8360	0.8321
0.5685	6.98	218	0.5089	0.8185	0.8195	0.8185	0.8163
0.5438	8.0	250	0.4806	0.8320	0.8325	0.8320	0.8293
0.5455	8.99	281	0.4781	0.8410	0.8442	0.8410	0.8406
0.5002	9.98	312	0.4403	0.8478	0.8457	0.8478	0.8452
0.5003	10.98	343	0.5079	0.8151	0.8355	0.8151	0.8156
0.4828	12.0	375	0.5307	0.8191	0.8197	0.8191	0.8147
0.4674	12.99	406	0.4732	0.8444	0.8450	0.8444	0.8431
0.4646	13.98	437	0.4952	0.8281	0.8342	0.8281	0.8251
0.4477	14.98	468	0.4607	0.8523	0.8516	0.8523	0.8514
0.4224	16.0	500	0.4514	0.8506	0.8516	0.8506	0.8495
0.3751	16.99	531	0.4665	0.8484	0.8480	0.8484	0.8472
0.3874	17.98	562	0.4462	0.8489	0.8489	0.8489	0.8477
0.3675	18.98	593	0.4674	0.8484	0.8508	0.8484	0.8471
0.3434	20.0	625	0.4644	0.8512	0.8486	0.8512	0.8462
0.3332	20.99	656	0.4711	0.8557	0.8548	0.8557	0.8525
0.3187	21.98	687	0.4665	0.8534	0.8539	0.8534	0.8524
0.3039	22.98	718	0.5015	0.8439	0.8427	0.8439	0.8423
0.282	24.0	750	0.4783	0.8563	0.8563	0.8563	0.8559
0.2843	24.99	781	0.5064	0.8534	0.8519	0.8534	0.8514
0.2661	25.98	812	0.5021	0.8484	0.8476	0.8484	0.8460
0.2595	26.98	843	0.4852	0.8613	0.8626	0.8613	0.8615
0.2442	28.0	875	0.4903	0.8568	0.8546	0.8568	0.8543
0.2477	28.99	906	0.4781	0.8585	0.8569	0.8585	0.8570
0.2437	29.76	930	0.4772	0.8585	0.8580	0.8585	0.8577

Framework versions

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