You can deploy this model and start earning money today!

Diamantis99/
manet_mit_b5

Name: manet_mit_b5
Rating: 2.5 (6 reviews)

segmentation-models-pytorch

safetensors

model_hub_mixin

pytorch_model_hub_mixin

semantic-segmentation

pytorch

image-segmentation

mit

Views

No views yet

MAnet Model Card

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_use_norm": "batchnorm",
    "decoder_channels": (256, 128, 64, 32, 16),
    "decoder_pab_channels": 64,
    "decoder_interpolation": "nearest",
    "in_channels": 3,
    "classes": 1,
    "activation": None,
    "aux_params": None
}

Model metrics

json

[
    {
        "test_per_image_iou": 0.5815715193748474,
        "test_dataset_iou": 0.671215832233429,
        "test_per_image_accuracy": 0.8453553318977356,
        "test_dataset_accuracy": 0.8453553318977356
    }
]

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/manet_mit_b5