README.md

metadata

library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
tags:
  - model_hub_mixin
  - pytorch_model_hub_mixin
  - segmentation-models-pytorch
  - semantic-segmentation
  - pytorch
languages:
  - python

Unet Model Card

Table of Contents:

• Load trained model
• Model init parameters
• Model metrics
• Dataset

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": "resnet34",
    "encoder_depth": 5,
    "encoder_weights": "imagenet",
    "decoder_use_batchnorm": True,
    "decoder_channels": (256, 128, 64, 32, 16),
    "decoder_attention_type": None,
    "in_channels": 3,
    "classes": 1,
    "activation": None,
    "aux_params": None
}

Model metrics

{
    "mIoU": 0.95,
    "accuracy": 0.96
}

Dataset

Dataset name: PASCAL VOC

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