metadata
library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
short_description: 5 classes image segmentation model trained on the comma10k dataset.
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- segmentation-models-pytorch
- semantic-segmentation
- pytorch
languages:
- python
Unet Model Card
Table of Contents:
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