wangzfsh/upernet-convnext-base-AIData-1115

upernet-convnext-base-AIData

This model is a fine-tuned version of openmmlab/upernet-convnext-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0004
• Mean Iou: 0.8811
• Mean Accuracy: 0.9308
• Overall Accuracy: 0.9999
• Per Category Iou: [0.9999341825925119, 0.7621714778112882]
• Per Category Accuracy: [0.9999680440896173, 0.861665854846566]

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
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Per Category Iou	Per Category Accuracy
0.0005	31.25	500	0.0004	0.8811	0.9308	0.9999	[0.9999341825925119, 0.7621714778112882]	[0.9999680440896173, 0.861665854846566]

Framework versions

• Transformers 4.46.2
• Pytorch 2.5.0+cu121
• Datasets 3.1.0
• Tokenizers 0.20.3