https://community.esri.com/t5/arcgis-pro-questions/seeking-guidance-achieving-desired-plot-level/m-p/1419550#M82845 <P><a href="https://community.esri.com/t5/user/viewprofilepage/user-id/787205">@Kwakuopokuware401</a>&nbsp;</P><P>I'm not 100% sure, but one possible reason could be the tile size of your training data. You can try these to see if you get better results:</P><UL><LI>Use a tile size that is 3 times larger than the average size of your object of interest.</LI><LI>Try "Output No Feature Tiles" (unchecked) - only image chips that capture training samples will be exported. This is the default.</LI><LI>Experiment with the "Minimum Polygon Overlap Ratio" parameter.</LI></UL><P>In the <A href="http://Export%20Training Data For Deep Learning (Image Analyst)—ArcGIS Pro | Documentation" target="_self"><STRONG>Export Training Data For Deep Learning (Image Analyst)</STRONG></A> tool within ArcGIS Pro, also make sure that the chip size is not smaller than the tile size in the <A href="https://pro.arcgis.com/en/pro-app/latest/tool-reference/image-analyst/train-deep-learning-model.htm" target="_self"><STRONG>Train Deep Learning Model (Image Analyst)</STRONG></A> tool.</P><P>You might find these two blogs useful:</P><UL><LI>Tips for labeling images for object detection models (<A href="https://www.esri.com/arcgis-blog/products/arcgis-pro/geoai/tips-for-labeling-images-for-object-detection-models/" target="_blank" rel="noopener noreferrer">https://www.esri.com/arcgis-blog/products/arcgis-pro/geoai/tips-for-labeling-images-for-object-detection-models/</A>)</LI><LI>Tips for training data preparation for object detection models (<A href="https://www.esri.com/arcgis-blog/products/arcgis-pro/geoai/tips-for-training-data-preparation-for-object-detection-models/" target="_blank" rel="noopener noreferrer">https://www.esri.com/arcgis-blog/products/arcgis-pro/geoai/tips-for-training-data-preparation-for-object-detection-models/</A>)</LI></UL><P>FYI, we have a dedicated board for ArcGIS Image Analyst questions. There, you may get answers sooner on imagery-related workflows.</P><P><BR />Cheers!<BR />Pavan</P><P>Pavan Yadav | Product Engineer - Raster/Imagery team</P><P>THE SCIENCE OF WHERE ®</P> Mon, 06 May 2024 18:10:30 GMT PavanYadav 2024-05-06T18:10:30Z https://community.esri.com/t5/arcgis-pro-questions/seeking-guidance-achieving-desired-plot-level/m-p/1411396#M82096 <P><SPAN>Hi</SPAN></P><P><SPAN>I hope this message finds you well. I am reaching out to you today to seek your guidance and expertise regarding a technical challenge I am currently facing with plot-level segmentation using MaskRCNN in ArcGIS Pro. I have recently been working on a project that involves training a model for plot-level segmentation. I have successfully prepared my training data using the ArcGIS training tool for deep learning and exported it with the appropriate metadata information. However, upon testing my trained model, I encountered an issue where the results are displayed in small chips, similar to the training labels, rather than the desired plot-level segmentation. My primary objective is to obtain segmentation results that align with the plots I have trained the model on. I would greatly appreciate any assistance or clarification you could provide to help me overcome this hurdle and achieve the desired output.</SPAN></P> Thu, 18 Apr 2024 05:53:34 GMT https://community.esri.com/t5/arcgis-pro-questions/seeking-guidance-achieving-desired-plot-level/m-p/1411396#M82096 Kwakuopokuware401 2024-04-18T05:53:34Z https://community.esri.com/t5/arcgis-pro-questions/seeking-guidance-achieving-desired-plot-level/m-p/1419550#M82845 <P><a href="https://community.esri.com/t5/user/viewprofilepage/user-id/787205">@Kwakuopokuware401</a>&nbsp;</P><P>I'm not 100% sure, but one possible reason could be the tile size of your training data. You can try these to see if you get better results:</P><UL><LI>Use a tile size that is 3 times larger than the average size of your object of interest.</LI><LI>Try "Output No Feature Tiles" (unchecked) - only image chips that capture training samples will be exported. This is the default.</LI><LI>Experiment with the "Minimum Polygon Overlap Ratio" parameter.</LI></UL><P>In the <A href="http://Export%20Training Data For Deep Learning (Image Analyst)—ArcGIS Pro | Documentation" target="_self"><STRONG>Export Training Data For Deep Learning (Image Analyst)</STRONG></A> tool within ArcGIS Pro, also make sure that the chip size is not smaller than the tile size in the <A href="https://pro.arcgis.com/en/pro-app/latest/tool-reference/image-analyst/train-deep-learning-model.htm" target="_self"><STRONG>Train Deep Learning Model (Image Analyst)</STRONG></A> tool.</P><P>You might find these two blogs useful:</P><UL><LI>Tips for labeling images for object detection models (<A href="https://www.esri.com/arcgis-blog/products/arcgis-pro/geoai/tips-for-labeling-images-for-object-detection-models/" target="_blank" rel="noopener noreferrer">https://www.esri.com/arcgis-blog/products/arcgis-pro/geoai/tips-for-labeling-images-for-object-detection-models/</A>)</LI><LI>Tips for training data preparation for object detection models (<A href="https://www.esri.com/arcgis-blog/products/arcgis-pro/geoai/tips-for-training-data-preparation-for-object-detection-models/" target="_blank" rel="noopener noreferrer">https://www.esri.com/arcgis-blog/products/arcgis-pro/geoai/tips-for-training-data-preparation-for-object-detection-models/</A>)</LI></UL><P>FYI, we have a dedicated board for ArcGIS Image Analyst questions. There, you may get answers sooner on imagery-related workflows.</P><P><BR />Cheers!<BR />Pavan</P><P>Pavan Yadav | Product Engineer - Raster/Imagery team</P><P>THE SCIENCE OF WHERE ®</P> Mon, 06 May 2024 18:10:30 GMT https://community.esri.com/t5/arcgis-pro-questions/seeking-guidance-achieving-desired-plot-level/m-p/1419550#M82845 PavanYadav 2024-05-06T18:10:30Z