pairs-classifier-20x-large-516-line100

This model is a fine-tuned version of facebook/dinov2-large-imagenet1k-1-layer on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.1753
• Accuracy: 0.6791
• F1: 0.6790
• Recall: 0.7219
• Precision: 0.7241

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: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Precision
0.7285	0.95	13	0.6921	0.6279	0.3857	0.3140	0.5
0.6882	1.96	27	0.6537	0.6372	0.5580	0.5947	0.5660
0.5771	2.98	41	0.6030	0.7070	0.7039	0.7141	0.7285
0.5086	4.0	55	0.5027	0.7395	0.7365	0.7453	0.7620
0.4418	4.95	68	0.6439	0.7023	0.7022	0.7652	0.7579
0.3432	5.96	82	0.6873	0.7116	0.7115	0.7755	0.7678
0.3346	6.98	96	0.4600	0.7721	0.7579	0.7563	0.7600
0.2755	8.0	110	0.8594	0.6884	0.6881	0.7528	0.7442
0.2676	8.95	123	0.9163	0.6884	0.6879	0.7588	0.7468
0.2658	9.96	137	0.5970	0.7442	0.7424	0.7575	0.7734
0.24	10.98	151	0.5749	0.7488	0.7454	0.7519	0.7694
0.234	12.0	165	0.8778	0.6977	0.6976	0.7517	0.7491
0.2154	12.95	178	0.8210	0.7023	0.7023	0.7490	0.7502
0.203	13.96	192	0.7305	0.7163	0.7154	0.7397	0.7512
0.1765	14.98	206	0.8578	0.6977	0.6976	0.7416	0.7440
0.1607	16.0	220	0.6878	0.7581	0.7543	0.7587	0.7769
0.1783	16.95	233	0.6481	0.7814	0.7764	0.7765	0.7954
0.1767	17.96	247	0.9805	0.6791	0.6790	0.7174	0.7215
0.1596	18.98	261	1.0490	0.6977	0.6976	0.7517	0.7491
0.152	20.0	275	1.1249	0.6744	0.6741	0.7402	0.7306
0.1807	20.95	288	0.8215	0.7302	0.7281	0.7419	0.7572
0.1441	21.96	302	1.2401	0.6791	0.6788	0.7425	0.7343
0.1494	22.98	316	0.9281	0.7023	0.7015	0.7273	0.7375
0.1303	24.0	330	0.8422	0.7256	0.7236	0.7387	0.7535
0.1324	24.95	343	0.9261	0.7070	0.7061	0.7302	0.7412
0.1214	25.96	357	1.1154	0.6791	0.6791	0.7266	0.7266
0.1184	26.98	371	1.2263	0.6698	0.6697	0.7267	0.7218
0.14	28.0	385	0.9725	0.7163	0.7154	0.7397	0.7512
0.107	28.95	398	0.9908	0.7163	0.7154	0.7397	0.7512
0.1082	29.96	412	1.1253	0.6698	0.6697	0.7216	0.7192
0.1477	30.98	426	0.7786	0.7581	0.7537	0.7564	0.7743
0.1296	32.0	440	1.5284	0.6558	0.6546	0.7377	0.7183
0.1122	32.95	453	1.0805	0.7070	0.7063	0.734	0.7437
0.0994	33.96	467	1.2907	0.6651	0.6651	0.7190	0.7155
0.1046	34.98	481	0.8528	0.7488	0.7454	0.7519	0.7694
0.0942	36.0	495	1.5078	0.6651	0.6645	0.7357	0.7231
0.1089	36.95	508	0.9448	0.7442	0.7415	0.7514	0.7683
0.096	37.96	522	1.2000	0.6651	0.6651	0.7094	0.7104
0.0903	38.98	536	0.9947	0.7163	0.7147	0.7326	0.7461
0.079	40.0	550	1.0776	0.7116	0.7106	0.7331	0.7449
0.0787	40.95	563	1.1923	0.6744	0.6744	0.7193	0.7204
0.0882	41.96	577	1.1516	0.6791	0.6788	0.7132	0.7190
0.0937	42.98	591	1.1568	0.6884	0.6881	0.7229	0.7289
0.0837	44.0	605	1.1792	0.6791	0.6790	0.7219	0.7241
0.0838	44.95	618	1.1944	0.6791	0.6790	0.7219	0.7241
0.085	45.96	632	1.0771	0.7163	0.7154	0.7397	0.7512
0.0781	46.98	646	1.1780	0.6791	0.6790	0.7219	0.7241
0.0828	47.27	650	1.1753	0.6791	0.6790	0.7219	0.7241

Framework versions

• Transformers 4.37.2
• Pytorch 2.2.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.1