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

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

• Loss: 0.5795
• Accuracy: 0.8546
• Precision: 0.8565
• Recall: 0.8546
• F1: 0.8545

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
1.0752	0.99	62	0.9584	0.6697	0.6820	0.6697	0.6581
0.814	2.0	125	0.6716	0.7728	0.7780	0.7728	0.7695
0.7289	2.99	187	0.6071	0.7959	0.8093	0.7959	0.7943
0.6444	4.0	250	0.5873	0.8055	0.8103	0.8055	0.8019
0.5855	4.99	312	0.5889	0.8106	0.8226	0.8106	0.8106
0.5778	6.0	375	0.5039	0.8281	0.8321	0.8281	0.8274
0.5575	6.99	437	0.5162	0.8140	0.8235	0.8140	0.8148
0.5011	8.0	500	0.5369	0.8207	0.8234	0.8207	0.8205
0.4968	8.99	562	0.5152	0.8292	0.8282	0.8292	0.8270
0.4593	10.0	625	0.4854	0.8382	0.8408	0.8382	0.8367
0.4442	10.99	687	0.4923	0.8416	0.8423	0.8416	0.8411
0.4071	12.0	750	0.5312	0.8377	0.8356	0.8377	0.8331
0.4057	12.99	812	0.4954	0.8433	0.8449	0.8433	0.8416
0.4074	14.0	875	0.4735	0.8534	0.8509	0.8534	0.8493
0.3709	14.99	937	0.4977	0.8461	0.8450	0.8461	0.8442
0.3467	16.0	1000	0.5364	0.8286	0.8278	0.8286	0.8274
0.3129	16.99	1062	0.5695	0.8422	0.8413	0.8422	0.8376
0.3242	18.0	1125	0.5131	0.8455	0.8469	0.8455	0.8450
0.3046	18.99	1187	0.5553	0.8399	0.8382	0.8399	0.8371
0.2805	20.0	1250	0.5871	0.8523	0.8532	0.8523	0.8456
0.2776	20.99	1312	0.5428	0.8433	0.8404	0.8433	0.8404
0.2975	22.0	1375	0.5624	0.8393	0.8344	0.8393	0.8359
0.268	22.99	1437	0.5485	0.8495	0.8518	0.8495	0.8498
0.2535	24.0	1500	0.6135	0.8382	0.8367	0.8382	0.8358
0.2543	24.99	1562	0.6103	0.8393	0.8389	0.8393	0.8375
0.2283	26.0	1625	0.5639	0.8484	0.8499	0.8484	0.8480
0.2341	26.99	1687	0.5795	0.8546	0.8565	0.8546	0.8545
0.2404	28.0	1750	0.5794	0.8534	0.8515	0.8534	0.8511
0.2168	28.99	1812	0.5652	0.8546	0.8525	0.8546	0.8524
0.2057	29.76	1860	0.5650	0.8546	0.8519	0.8546	0.8518

Framework versions

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