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docs: Typo fixes
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README.md

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@@ -182,7 +182,7 @@ The class API enables the most flexibility in defining different model architect
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- `model.{head_name}_seg_head` - Model decoders can have multiple segmentation heads with unique names.
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- `model.forward(x)` - forward pass.
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**Defining you own multi-task architecture**
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**Defining your own multi-task architecture**
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For example, to define a multi-task architecture that has `resnet50` encoder, four decoders, and 5 output heads with `CellPoseUnet` architectural components, we could do this:
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cellseg_models_pytorch/datasets/tests/test_dataset.py

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inst_transforms = ["smooth_dist"]
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@pytest.mark.optional
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# @pytest.mark.optional
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@pytest.mark.parametrize("return_inst", [True, False])
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@pytest.mark.parametrize("return_type", [True, False])
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@pytest.mark.parametrize("return_sem", [True, False])
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## Docs
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- Typo fies in docs

examples/lizard_nuclei_segmentation_cellpose.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"import pytorch_lightning as pl\n",
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"import cellseg_models_pytorch as csmp\n",
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"from pathlib import Path\n",
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"from cellseg_models_pytorch.datamodules import LizardDataModule\n",
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"\n",
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"fold_split = {\"train\": 1, \"valid\": 2, \"test\": 3}\n",
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"save_dir = Path.home() / \"lizard\" # modify the save path\n",
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"lizard_module = LizardDataModule(\n",
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" save_dir=save_dir,\n",
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" fold_split=fold_split,\n",
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" inst_transforms=[\"cellpose\"],\n",
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" img_transforms=[\"blur\", \"hue_sat\"],\n",
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" patch_size=(320, 320),\n",
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" stride=220,\n",
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" normalization=\"minmax\",\n",
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")\n",
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"\n",
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"\n",
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"model = csmp.models.cellpose_base(\n",
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" enc_name=\"tf_efficientnetv2_s\",\n",
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" type_classes=len(lizard_module.type_classes),\n",

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