gJZBe9X

Diamantis99/gJZBe9X

gJZBe9X is a image segmentation model from Diamantis99. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for segmentation-models-pytorch. The card lists the license as mit.

Table of Contents: - Load trained model - Model init parameters - Model metrics - Dataset

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At a glance

Task: Image Segmentation
Library: segmentation-models-pytorch
License: mit
Access: Public
Created: May 28, 2025
Updated: May 28, 2025
SHA: 30d52747

Model card

From the Hugging Face model README

DeepLabV3Plus 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": "mobileone_s4",
    "encoder_depth": 5,
    "encoder_weights": "imagenet",
    "encoder_output_stride": 16,
    "decoder_channels": 256,
    "decoder_atrous_rates": (12, 24, 36),
    "decoder_aspp_separable": True,
    "decoder_aspp_dropout": 0.5,
    "in_channels": 3,
    "classes": 1,
    "activation": None,
    "upsampling": 4,
    "aux_params": None
}

Model metrics

[
    {
        "test_per_image_iou": 0.8020665049552917,
        "test_dataset_iou": 0.854163646697998
    }
]

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

Task: Image Segmentation
Library: segmentation-models-pytorch
License: mit
Created: May 28, 2025
Updated: May 28, 2025