jpodivin/upernet-swin-small-finetuned

来源：🤗有风险🏴‍☠️ 救援者：isiliconimage-segmentationmit81M 参数325 MBsafetensors✓ 2 个校验和980 天前更新

upernet-swin-small-finetuned

This model is a fine-tuned version of openmmlab/upernet-swin-small on the jpodivin/plantorgans dataset. It achieves the following results on the evaluation set:

Loss: 0.2914
Mean Iou: 0.4182
Mean Accuracy: 0.5282
Overall Accuracy: 0.7341
Accuracy Void: nan
Accuracy Fruit: 0.8590
Accuracy Leaf: 0.7032
Accuracy Flower: 0.0
Accuracy Stem: 0.5505
Iou Void: 0.0
Iou Fruit: 0.8554
Iou Leaf: 0.6976
Iou Flower: 0.0
Iou Stem: 0.5381
Median Iou: 0.5381

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: 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

Training Loss	Epoch	Step	Validation Loss	Mean Iou	Mean Accuracy	Overall Accuracy	Accuracy Void	Accuracy Fruit	Accuracy Leaf	Accuracy Stem	Iou Fruit	Iou Leaf	Iou Stem	Median Iou
0.8566	1.0	575	0.3365	0.3723	0.4705	0.6560	nan	0.8000	0.6122	0.4699	0.7976	0.6041	0.4598	0.4598
0.3338	2.0	1150	0.3030	0.3922	0.4937	0.7155	nan	0.8558	0.7024	0.4166	0.8517	0.6972	0.4119	0.4119
0.3477	3.0	1725	0.2914	0.4182	0.5282	0.7341	nan	0.8590	0.7032	0.5505	0.8554	0.6976	0.5381	0.5381

Framework versions

Transformers 4.38.0.dev0
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0