swinv2-base-patch4-window8-256-finetuned-ind-17-imbalanced-aadhaarmask

This model is a fine-tuned version of microsoft/swinv2-base-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.3463
• Accuracy: 0.8463
• Recall: 0.8463
• F1: 0.8464
• Precision: 0.8482

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	F1	Precision
No log	0.9974	293	0.6222	0.7901	0.7901	0.7737	0.7747
No log	1.9983	587	0.4901	0.8063	0.8063	0.7998	0.8066
No log	2.9991	881	0.4374	0.8225	0.8225	0.8170	0.8356
No log	4.0	1175	0.4262	0.8340	0.8340	0.8270	0.8541
No log	4.9974	1468	0.4079	0.8310	0.8310	0.8290	0.8379
No log	5.9983	1762	0.4117	0.8370	0.8370	0.8361	0.8509
No log	6.9991	2056	0.3807	0.8370	0.8370	0.8361	0.8416
No log	8.0	2350	0.3419	0.8595	0.8595	0.8583	0.8609
No log	8.9974	2643	0.3628	0.8438	0.8438	0.8424	0.8448
0.4492	9.9745	2930	0.3638	0.8399	0.8399	0.8394	0.8410

Framework versions

• Transformers 4.40.1
• Pytorch 2.2.0a0+81ea7a4
• Datasets 2.19.0
• Tokenizers 0.19.1