vit-large-patch16-224-in21k-finetuned-galaxy10-decals

This model is a fine-tuned version of google/vit-large-patch16-224-in21k on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:

• Loss: 0.6167
• Accuracy: 0.8455
• Precision: 0.8422
• Recall: 0.8455
• F1: 0.8432

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: 0.0001
• 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
1.1548	0.99	31	0.8377	0.7238	0.7320	0.7238	0.6999
0.7125	1.98	62	0.6551	0.7847	0.7914	0.7847	0.7807
0.6646	2.98	93	0.5389	0.8106	0.8079	0.8106	0.8024
0.5941	4.0	125	0.5209	0.8247	0.8319	0.8247	0.8134
0.5485	4.99	156	0.5222	0.8253	0.8317	0.8253	0.8249
0.4856	5.98	187	0.5064	0.8298	0.8324	0.8298	0.8241
0.4613	6.98	218	0.4739	0.8439	0.8464	0.8439	0.8418
0.4395	8.0	250	0.4993	0.8348	0.8449	0.8348	0.8359
0.4184	8.99	281	0.5139	0.8388	0.8386	0.8388	0.8339
0.3635	9.98	312	0.5031	0.8348	0.8372	0.8348	0.8349
0.3665	10.98	343	0.5235	0.8337	0.8330	0.8337	0.8319
0.3639	12.0	375	0.5032	0.8433	0.8471	0.8433	0.8429
0.344	12.99	406	0.5632	0.8326	0.8366	0.8326	0.8295
0.3089	13.98	437	0.5355	0.8433	0.8459	0.8433	0.8415
0.2947	14.98	468	0.5596	0.8371	0.8379	0.8371	0.8363
0.279	16.0	500	0.5364	0.8433	0.8403	0.8433	0.8396
0.2465	16.99	531	0.5636	0.8377	0.8360	0.8377	0.8359
0.2404	17.98	562	0.6250	0.8343	0.8391	0.8343	0.8334
0.2365	18.98	593	0.5921	0.8410	0.8416	0.8410	0.8405
0.2307	20.0	625	0.6118	0.8393	0.8383	0.8393	0.8378
0.2217	20.99	656	0.6195	0.8410	0.8399	0.8410	0.8389
0.2104	21.98	687	0.6108	0.8326	0.8330	0.8326	0.8315
0.2079	22.98	718	0.6308	0.8405	0.8375	0.8405	0.8373
0.1784	24.0	750	0.6167	0.8455	0.8422	0.8455	0.8432
0.1901	24.99	781	0.6394	0.8439	0.8411	0.8439	0.8416
0.1801	25.98	812	0.6667	0.8348	0.8349	0.8348	0.8342
0.1892	26.98	843	0.6335	0.8393	0.8410	0.8393	0.8394
0.1756	28.0	875	0.6431	0.8455	0.8439	0.8455	0.8440
0.1791	28.99	906	0.6445	0.8444	0.8423	0.8444	0.8428
0.1728	29.76	930	0.6451	0.8439	0.8414	0.8439	0.8418

Framework versions

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