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

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

• Loss: 0.3601
• Accuracy: 0.8565
• Recall: 0.8565
• F1: 0.8537
• Precision: 0.8631

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	0.6645	0.7820	0.7820	0.7661	0.7678
No log	1.9983	587	0.5493	0.8033	0.8033	0.7897	0.7964
No log	2.9991	881	0.4242	0.8416	0.8416	0.8380	0.8460
No log	4.0	1175	0.4124	0.8310	0.8310	0.8288	0.8299
No log	4.9974	1468	0.3769	0.8412	0.8412	0.8388	0.8478
No log	5.9983	1762	0.3589	0.8501	0.8501	0.8481	0.8582
No log	6.9991	2056	0.3503	0.8455	0.8455	0.8456	0.8535
No log	8.0	2350	0.3400	0.8404	0.8404	0.8416	0.8465
No log	8.9974	2643	0.3533	0.8480	0.8480	0.8480	0.8501
0.5214	9.9745	2930	0.3358	0.8459	0.8459	0.8460	0.8473

Framework versions

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