Push model using huggingface_hub.
Browse files- README.md +55 -0
- config.json +19 -0
- model.safetensors +3 -0
README.md
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---
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library_name: segmentation-models-pytorch
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license: mit
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pipeline_tag: image-segmentation
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tags:
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- model_hub_mixin
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- pytorch_model_hub_mixin
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- segmentation-models-pytorch
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- semantic-segmentation
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- pytorch
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languages:
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- python
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---
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# Unet Model Card
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Table of Contents:
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- [Load trained model](#load-trained-model)
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- [Model init parameters](#model-init-parameters)
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- [Model metrics](#model-metrics)
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- [Dataset](#dataset)
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## Load trained model
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```python
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import segmentation_models_pytorch as smp
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model = smp.from_pretrained("<save-directory-or-this-repo>")
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```
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## Model init parameters
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```python
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model_init_params = {
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"encoder_name": "resnet34",
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"encoder_depth": 5,
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"encoder_weights": "imagenet",
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"decoder_use_batchnorm": True,
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"decoder_channels": (256, 128, 64, 32, 16),
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"decoder_attention_type": None,
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"in_channels": 3,
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"classes": 1,
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"activation": None,
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"aux_params": None
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}
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```
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## Model metrics
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[More Information Needed]
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## Dataset
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Dataset name: [More Information Needed]
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## More Information
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- Library: https://github.com/qubvel/segmentation_models.pytorch
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- Docs: https://smp.readthedocs.io/en/latest/
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This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
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config.json
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{
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"_model_class": "Unet",
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"activation": null,
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"aux_params": null,
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"classes": 1,
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"decoder_attention_type": null,
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"decoder_channels": [
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256,
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128,
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64,
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32,
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16
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],
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"decoder_use_batchnorm": true,
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"encoder_depth": 5,
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"encoder_name": "resnet34",
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"encoder_weights": "imagenet",
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"in_channels": 3
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:aa2bb6d888c0ce9f9a36305baabfc1f766fecfd5ad9dcc10163c74b6a30f59bf
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size 97849364
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