From 43f094372631d65d8bad13f909eded94cb97dcf3 Mon Sep 17 00:00:00 2001 From: minsu Date: Mon, 22 Sep 2025 17:25:24 +0900 Subject: [PATCH 1/2] docs: fix typo UNet_meatdata -> UNet_metadata --- .../spleen_segmentation_3d_visualization_basic.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/3d_segmentation/spleen_segmentation_3d_visualization_basic.ipynb b/3d_segmentation/spleen_segmentation_3d_visualization_basic.ipynb index e1b1749d3..f7ec118cb 100644 --- a/3d_segmentation/spleen_segmentation_3d_visualization_basic.ipynb +++ b/3d_segmentation/spleen_segmentation_3d_visualization_basic.ipynb @@ -484,7 +484,7 @@ "# standard PyTorch program style: create UNet, DiceLoss and Adam optimizer\n", "device = torch.device(\"cuda:0\")\n", "\n", - "UNet_meatdata = {\n", + "UNet_metadata = {\n", " \"spatial_dims\": 3,\n", " \"in_channels\": 1,\n", " \"out_channels\": 2,\n", @@ -494,7 +494,7 @@ " \"norm\": Norm.BATCH,\n", "}\n", "\n", - "model = UNet(**UNet_meatdata).to(device)\n", + "model = UNet(**UNet_metadata).to(device)\n", "loss_function = DiceLoss(to_onehot_y=True, softmax=True)\n", "loss_type = \"DiceLoss\"\n", "optimizer = torch.optim.Adam(model.parameters(), 1e-4)\n", @@ -539,7 +539,7 @@ "# initialize a new Aim Run\n", "aim_run = aim.Run()\n", "# log model metadata\n", - "aim_run[\"UNet_meatdata\"] = UNet_meatdata\n", + "aim_run[\"UNet_metadata\"] = UNet_metadata\n", "# log optimizer metadata\n", "aim_run[\"Optimizer_metadata\"] = Optimizer_metadata\n", "\n", From 819954cc51aefc0c3de51cf551fc96191e08dd8f Mon Sep 17 00:00:00 2001 From: minsu Date: Mon, 29 Sep 2025 15:38:05 +0900 Subject: [PATCH 2/2] docs: minor fix for DCO remediation minsu DCO Remediation Commit for minsu I, minsu , hereby add my Signed-off-by to this commit: 43f094372631d65d8bad13f909eded94cb97dcf3 Signed-off-by: minsu --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index e23db92d9..48ebb87e9 100644 --- a/README.md +++ b/README.md @@ -386,3 +386,4 @@ Example shows the use cases of using MONAI to evaluate the performance of a gene #### [VISTA2D](./vista_2d) This tutorial demonstrates how to train a cell segmentation model using the [MONAI](https://monai.io/) framework and the [Segment Anything Model (SAM)](https://github.com/facebookresearch/segment-anything) on the [Cellpose dataset](https://www.cellpose.org/). +ECHO가 설정되어 있습니다.