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unetplusplus_timm-efficientnet-b8
Diamantis99/unetplusplus_timm-efficientnet-b8
unetplusplus_timm-efficientnet-b8 is a image segmentation model from Diamantis99. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for segmentation-models-pytorch. The card lists the license as mit.
Table of Contents: - Load trained model - Model init parameters - Model metrics - Dataset
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At a glance
- Task
- Image Segmentation
- Library
- segmentation-models-pytorch
- License
- mit
- Access
- Public
- Created
- Oct 30, 2025
- Updated
- Nov 3, 2025
- SHA
- 66466d7f
Model card
From the Hugging Face model README
UnetPlusPlus Model Card
Table of Contents:
Load trained model
import segmentation_models_pytorch as smp
model = smp.from_pretrained("<save-directory-or-this-repo>")
Model init parameters
model_init_params = {
"encoder_name": "timm-efficientnet-b8",
"encoder_depth": 5,
"encoder_weights": "imagenet",
"decoder_use_norm": "batchnorm",
"decoder_channels": (256, 128, 64, 32, 16),
"decoder_attention_type": None,
"decoder_interpolation": "nearest",
"in_channels": 3,
"classes": 1,
"activation": None,
"aux_params": None
}
Model metrics
[
{
"test_per_image_iou": 0.21585744619369507,
"test_dataset_iou": 0.2068282663822174,
"test_per_image_accuracy": 0.9685878753662109,
"test_dataset_accuracy": 0.9685878753662109
}
]
Dataset
Dataset name: VIP
More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
This model has been pushed to the Hub using the PytorchModelHubMixin
- Task
- Image Segmentation
- Library
- segmentation-models-pytorch
- License
- mit
- Created
- Oct 30, 2025
- Updated
- Nov 3, 2025