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Diamantis99/
RCQ36j8

Name: RCQ36j8
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": "timm-efficientnet-b7",
    "encoder_depth": 5,
    "encoder_weights": "imagenet",
    "encoder_output_stride": 8,
    "decoder_channels": 256,
    "decoder_atrous_rates": (12, 24, 36),
    "decoder_aspp_separable": False,
    "decoder_aspp_dropout": 0.5,
    "in_channels": 3,
    "classes": 1,
    "activation": None,
    "upsampling": None,
    "aux_params": None
}

Model metrics

json

[
    {
        "test_per_image_iou": 0.8510321378707886,
        "test_dataset_iou": 0.8771003484725952
    }
]

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/RCQ36j8