estudiante_Swin3D_profesor_MViT_akl_VIOPERU

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4529
• Accuracy: 0.8482
• F1: 0.8479
• Precision: 0.8510
• Recall: 0.8482
• Roc Auc: 0.9011

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: 1e-05
• train_batch_size: 10
• eval_batch_size: 10
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 66
• training_steps: 660
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Roc Auc
2.6385	1.0136	33	0.6857	0.6071	0.5497	0.7188	0.6071	0.6901
2.3351	2.0273	66	0.6273	0.6607	0.6597	0.6626	0.6607	0.7462
1.9551	4.0045	99	0.5946	0.7679	0.7660	0.7767	0.7679	0.7985
1.6791	5.0182	132	0.5445	0.6964	0.6963	0.6967	0.6964	0.8087
1.2876	6.0318	165	0.5660	0.6964	0.6940	0.7029	0.6964	0.7946
0.9364	8.0091	198	0.5349	0.7857	0.7857	0.7857	0.7857	0.8406
0.7493	9.0227	231	0.5149	0.7321	0.7300	0.7398	0.7321	0.8265
0.7771	10.0364	264	0.4977	0.7321	0.7300	0.7398	0.7321	0.8431
0.915	12.0136	297	0.4538	0.7857	0.7846	0.7917	0.7857	0.8763
0.7308	13.0273	330	0.4692	0.7857	0.7857	0.7857	0.7857	0.8584
0.7293	15.0045	363	0.4296	0.8036	0.8035	0.8040	0.8036	0.8724
0.607	16.0182	396	0.4088	0.8214	0.8205	0.8281	0.8214	0.9056
0.5406	17.0318	429	0.4907	0.8393	0.8388	0.8432	0.8393	0.9031
0.5116	19.0091	462	0.4292	0.8393	0.8392	0.8397	0.8393	0.9043
0.48	20.0227	495	0.3763	0.8571	0.8570	0.8590	0.8571	0.9145
0.4789	21.0364	528	0.4117	0.8214	0.8214	0.8214	0.8214	0.8954
0.4582	23.0136	561	0.4264	0.8214	0.8205	0.8281	0.8214	0.8916
0.4655	24.0273	594	0.4742	0.8036	0.8035	0.8040	0.8036	0.8839
0.4795	26.0045	627	0.5309	0.8036	0.8020	0.8136	0.8036	0.8929
0.4766	27.0182	660	0.4786	0.8036	0.8035	0.8040	0.8036	0.8980

Framework versions

• Transformers 4.46.1
• Pytorch 2.0.1+cu118
• Datasets 3.1.0
• Tokenizers 0.20.1