convnextv2-femto-1k-224-finetuned-galaxy10-decals

This model is a fine-tuned version of facebook/convnextv2-femto-1k-224 on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:

• Loss: 0.4463
• Accuracy: 0.8551
• Precision: 0.8509
• Recall: 0.8551
• F1: 0.8514

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
1.7326	0.99	62	1.6140	0.4758	0.4530	0.4758	0.4312
1.1706	2.0	125	1.0827	0.6218	0.6294	0.6218	0.5983
0.9046	2.99	187	0.7418	0.7542	0.7566	0.7542	0.7351
0.7305	4.0	250	0.6540	0.7880	0.7823	0.7880	0.7789
0.6378	4.99	312	0.5903	0.8089	0.8054	0.8089	0.8047
0.6447	6.0	375	0.5915	0.7954	0.8041	0.7954	0.7865
0.6228	6.99	437	0.5513	0.8162	0.8201	0.8162	0.8164
0.5758	8.0	500	0.5553	0.8078	0.8094	0.8078	0.8033
0.5831	8.99	562	0.5207	0.8191	0.8246	0.8191	0.8162
0.537	10.0	625	0.4981	0.8286	0.8233	0.8286	0.8222
0.5322	10.99	687	0.4830	0.8337	0.8340	0.8337	0.8332
0.5171	12.0	750	0.4931	0.8253	0.8258	0.8253	0.8233
0.5092	12.99	812	0.4891	0.8360	0.8360	0.8360	0.8325
0.5245	14.0	875	0.4585	0.8450	0.8452	0.8450	0.8431
0.4585	14.99	937	0.4682	0.8422	0.8407	0.8422	0.8407
0.455	16.0	1000	0.4659	0.8388	0.8370	0.8388	0.8357
0.4175	16.99	1062	0.4633	0.8382	0.8363	0.8382	0.8351
0.4574	18.0	1125	0.4479	0.8450	0.8435	0.8450	0.8428
0.4593	18.99	1187	0.4577	0.8439	0.8446	0.8439	0.8430
0.4423	20.0	1250	0.4589	0.8461	0.8426	0.8461	0.8413
0.4141	20.99	1312	0.4732	0.8326	0.8339	0.8326	0.8299
0.4534	22.0	1375	0.4477	0.8461	0.8422	0.8461	0.8433
0.4011	22.99	1437	0.4614	0.8399	0.8403	0.8399	0.8390
0.4162	24.0	1500	0.4576	0.8450	0.8443	0.8450	0.8437
0.4291	24.99	1562	0.4609	0.8472	0.8441	0.8472	0.8439
0.3698	26.0	1625	0.4469	0.8506	0.8484	0.8506	0.8482
0.3957	26.99	1687	0.4488	0.8478	0.8464	0.8478	0.8464
0.4053	28.0	1750	0.4463	0.8551	0.8509	0.8551	0.8514
0.377	28.99	1812	0.4429	0.8540	0.8504	0.8540	0.8508
0.381	29.76	1860	0.4423	0.8517	0.8483	0.8517	0.8489

Framework versions

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