metadata

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

FPN 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.FPN.from_pretrained("GID-segmentation-FPN_resnet101")

Model init parameters

model_init_params = {
    "encoder_name": "resnet101",
    "encoder_depth": 5,
    "encoder_weights": "imagenet",
    "decoder_pyramid_channels": 256,
    "decoder_segmentation_channels": 128,
    "decoder_merge_policy": "add",
    "decoder_dropout": 0.2,
    "in_channels": 4,
    "classes": 1,
    "activation": None,
    "upsampling": 4,
    "aux_params": None
}

Model metrics

[
    {
        "test_per_image_iou": 0.6290831565856934,
        "test_dataset_iou": 0.7467864155769348
    }
]

Dataset

Dataset name: GID

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