Armature_Defect_Detection_Resin

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.4978
• Accuracy: 0.76
• F1: 0.76
• Recall: 0.76
• Precision: 0.76

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: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Precision
No log	0.57	1	0.7205	0.44	0.44	0.44	0.44
No log	1.57	2	0.5926	0.6	0.6	0.6	0.6
No log	2.57	3	0.4978	0.76	0.76	0.76	0.76

Framework versions

• Transformers 4.23.1
• Pytorch 1.13.0
• Datasets 2.6.1
• Tokenizers 0.13.1