Unet Model Card

Table of Contents:

• Load trained model
• Model init parameters
• Model metrics
• Dataset

Load trained model

import segmentation_models_pytorch as smp
import albumentations as A

hub_repo = "commaai/comma10k-segnet"
model = smp.from_pretrained(hub_repo)
transform = A.Compose.from_pretrained(hub_repo)

Model init parameters

model_init_params = {
    "encoder_name": "tu-efficientnet_b2",
    "encoder_depth": 5,
    "encoder_weights": None,
    "decoder_use_norm": "batchnorm",
    "decoder_channels": (256, 128, 64, 32, 16),
    "decoder_attention_type": None,
    "decoder_interpolation": "nearest",
    "in_channels": 3,
    "classes": 5,
    "activation": None,
    "aux_params": None
}

Dataset

Dataset name: comma10k

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