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Diamantis99/
Z9kEgl5

Name: Z9kEgl5
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": "se_resnext101_32x4d",
    "encoder_depth": 5,
    "encoder_weights": "imagenet",
    "decoder_pyramid_channels": 256,
    "decoder_segmentation_channels": 128,
    "decoder_merge_policy": "add",
    "decoder_dropout": 0.2,
    "decoder_interpolation": "nearest",
    "in_channels": 3,
    "classes": 1,
    "activation": None,
    "upsampling": 4,
    "aux_params": None
}

Model metrics

json

[
    {
        "test_per_image_iou": 0.8645341992378235,
        "test_dataset_iou": 0.8839250802993774
    }
]

Dataset

Dataset name: VisionPipe

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/Z9kEgl5