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Diamantis99/
upernet_mit_b5

Name: upernet_mit_b5
Availability: InStock

segmentation-models-pytorch

safetensors

model_hub_mixin

pytorch_model_hub_mixin

semantic-segmentation

pytorch

image-segmentation

mit

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Table of Contents:

Load trained model
Model init parameters
Model metrics
Dataset

Load trained model

python

import segmentation_models_pytorch as smp

model = smp.from_pretrained("<save-directory-or-this-repo>")

Model init parameters

python

model_init_params = {
    "encoder_name": "mit_b5",
    "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

json

[
    {
        "test_per_image_iou": 0.233259916305542,
        "test_dataset_iou": 0.14191101491451263,
        "test_per_image_accuracy": 0.9656733870506287,
        "test_dataset_accuracy": 0.9656733870506287
    }
]

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

Diamantis99/upernet_mit_b5