Diamantis99/upernet_resnext101_32x8d

UPerNet 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": "resnext101_32x8d",
    "encoder_depth": 5,
    "encoder_weights": "imagenet",
    "decoder_channels": 256,
    "decoder_use_norm": "batchnorm",
    "in_channels": 3,
    "classes": 1,
    "activation": None,
    "upsampling": 4,
    "aux_params": None
}

Model metrics

[
    {
        "test_per_image_iou": 0.20957648754119873,
        "test_dataset_iou": 0.2279270589351654,
        "test_per_image_accuracy": 0.9694068431854248,
        "test_dataset_accuracy": 0.96940678358078
    }
]

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