upernet-swin-small-finetuned

• Loss: 0.2923
• Mean Iou: 0.4108
• Mean Accuracy: 0.5178
• Overall Accuracy: 0.7337
• Accuracy Void: nan
• Accuracy Fruit: 0.8691
• Accuracy Leaf: 0.7085
• Accuracy Flower: 0.0
• Accuracy Stem: 0.4937
• Iou Void: 0.0
• Iou Fruit: 0.8653
• Iou Leaf: 0.7034
• Iou Flower: 0.0
• Iou Stem: 0.4855
• Median Iou: 0.4855

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 0.0006
• train_batch_size: 10
• eval_batch_size: 10
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.2+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0