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library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- segmentation-models-pytorch
- semantic-segmentation
- pytorch
- archaeology
- archeology
- cave
- Ramps
- Unet++
- UnetPlusPlus
- U-Net++
languages:
- python
---
# Powerful Archaeological Segmentation Service for Erasing Ramps Enabled by Learning (PASSEREL)
# UnetPlusPlus Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#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": "resnext50_32x4d",
"encoder_depth": 5,
"encoder_weights": "swsl",
"decoder_use_norm": "batchnorm",
"decoder_channels": (256, 128, 64, 32, 16),
"decoder_attention_type": None,
"decoder_interpolation": "nearest",
"in_channels": 3,
"classes": 1,
"activation": None,
"aux_params": None
}
```
but
```python
"activation": sigmoide
```
makes a better result if you use the result not for metrics.
## Model metrics
[More Information Needed]
## Dataset
Dataset name: [More Information Needed]
## More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
- Script : https://github.com/mchedor/archeology_binary_segmentation_model
- Napasserelle : https://github.com/mchedor/Napasserelle
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) |