vit-large-patch32-384-finetuned-galaxy10-decals

This model is a fine-tuned version of google/vit-large-patch32-384 on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:

• Loss: 0.6766
• Accuracy: 0.8371
• Precision: 0.8374
• Recall: 0.8371
• F1: 0.8357

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
1.3342	0.99	31	1.0491	0.6313	0.6077	0.6313	0.6052
0.7979	1.98	62	0.6901	0.7672	0.7717	0.7672	0.7652
0.7197	2.98	93	0.6200	0.7785	0.7716	0.7785	0.7705
0.6321	4.0	125	0.5693	0.8061	0.8035	0.8061	0.7957
0.5768	4.99	156	0.5501	0.8112	0.8213	0.8112	0.8134
0.5173	5.98	187	0.5165	0.8213	0.8306	0.8213	0.8202
0.4781	6.98	218	0.5220	0.8106	0.8161	0.8106	0.8090
0.451	8.0	250	0.5133	0.8185	0.8227	0.8185	0.8153
0.4373	8.99	281	0.5118	0.8303	0.8325	0.8303	0.8288
0.3826	9.98	312	0.5280	0.8258	0.8269	0.8258	0.8243
0.378	10.98	343	0.5477	0.8174	0.8156	0.8174	0.8142
0.3509	12.0	375	0.5437	0.8281	0.8292	0.8281	0.8244
0.3358	12.99	406	0.5627	0.8258	0.8268	0.8258	0.8241
0.3027	13.98	437	0.5558	0.8326	0.8341	0.8326	0.8310
0.3027	14.98	468	0.5703	0.8326	0.8358	0.8326	0.8295
0.2786	16.0	500	0.5791	0.8281	0.8268	0.8281	0.8249
0.2379	16.99	531	0.5864	0.8275	0.8264	0.8275	0.8251
0.2426	17.98	562	0.5984	0.8320	0.8320	0.8320	0.8305
0.2325	18.98	593	0.6217	0.8264	0.8281	0.8264	0.8252
0.2208	20.0	625	0.6166	0.8258	0.8230	0.8258	0.8236
0.2196	20.99	656	0.6308	0.8286	0.8280	0.8286	0.8259
0.2077	21.98	687	0.6242	0.8326	0.8307	0.8326	0.8305
0.2048	22.98	718	0.6801	0.8275	0.8303	0.8275	0.8263
0.1886	24.0	750	0.6615	0.8264	0.8280	0.8264	0.8256
0.2007	24.99	781	0.6847	0.8275	0.8280	0.8275	0.8267
0.1815	25.98	812	0.6669	0.8326	0.8311	0.8326	0.8305
0.1958	26.98	843	0.6766	0.8371	0.8374	0.8371	0.8357
0.1806	28.0	875	0.6679	0.8360	0.8353	0.8360	0.8342
0.1835	28.99	906	0.6767	0.8348	0.8334	0.8348	0.8328
0.1796	29.76	930	0.6787	0.8343	0.8336	0.8343	0.8326

Framework versions

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