SegFormer_mit-b5_Final-Set4-Grayscale_Test_4_lr0.0001

• Loss: 0.1193
• Mean Iou: 0.8167
• Mean Accuracy: 0.8517
• Overall Accuracy: 0.9601
• Accuracy Background: 0.9780
• Accuracy Melt: 0.5944
• Accuracy Substrate: 0.9827
• Iou Background: 0.9616
• Iou Melt: 0.5614
• Iou Substrate: 0.9270

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 0.0001
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 1
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.41.2
• Pytorch 2.0.1+cu117
• Datasets 2.19.2
• Tokenizers 0.19.1