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

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

• Loss: 0.4668
• Accuracy: 0.8461
• Precision: 0.8444
• Recall: 0.8461
• F1: 0.8442

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
2.0062	0.99	62	1.8928	0.3450	0.3432	0.3450	0.2956
1.1323	2.0	125	1.0026	0.6590	0.6634	0.6590	0.6399
0.8977	2.99	187	0.7348	0.7486	0.7415	0.7486	0.7399
0.7119	4.0	250	0.6395	0.7892	0.7878	0.7892	0.7770
0.6393	4.99	312	0.5801	0.7971	0.7916	0.7971	0.7915
0.6463	6.0	375	0.5958	0.7976	0.8147	0.7976	0.7909
0.6197	6.99	437	0.5363	0.8151	0.8119	0.8151	0.8112
0.5779	8.0	500	0.5276	0.8207	0.8205	0.8207	0.8185
0.5841	8.99	562	0.5197	0.8185	0.8203	0.8185	0.8157
0.5597	10.0	625	0.5025	0.8253	0.8192	0.8253	0.8193
0.5437	10.99	687	0.4912	0.8309	0.8295	0.8309	0.8296
0.5242	12.0	750	0.5001	0.8275	0.8303	0.8275	0.8245
0.5029	12.99	812	0.5075	0.8241	0.8228	0.8241	0.8208
0.5396	14.0	875	0.4784	0.8393	0.8395	0.8393	0.8371
0.4746	14.99	937	0.4727	0.8331	0.8318	0.8331	0.8317
0.4786	16.0	1000	0.4856	0.8331	0.8308	0.8331	0.8300
0.4338	16.99	1062	0.4884	0.8337	0.8333	0.8337	0.8309
0.4772	18.0	1125	0.4618	0.8405	0.8370	0.8405	0.8377
0.4733	18.99	1187	0.4740	0.8393	0.8394	0.8393	0.8381
0.4475	20.0	1250	0.4678	0.8388	0.8349	0.8388	0.8345
0.4229	20.99	1312	0.4881	0.8331	0.8317	0.8331	0.8303
0.46	22.0	1375	0.4728	0.8410	0.8382	0.8410	0.8371
0.4298	22.99	1437	0.4642	0.8360	0.8348	0.8360	0.8345
0.4225	24.0	1500	0.4706	0.8371	0.8368	0.8371	0.8359
0.426	24.99	1562	0.4733	0.8399	0.8367	0.8399	0.8371
0.3839	26.0	1625	0.4682	0.8444	0.8423	0.8444	0.8422
0.4007	26.99	1687	0.4665	0.8382	0.8371	0.8382	0.8367
0.4245	28.0	1750	0.4695	0.8388	0.8357	0.8388	0.8358
0.3868	28.99	1812	0.4668	0.8461	0.8444	0.8461	0.8442
0.3933	29.76	1860	0.4657	0.8461	0.8442	0.8461	0.8440

Framework versions

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