Latest Contributions by PriyankaTuteja

@kumarprince8071  This is not supported through our product offerings currently. In case you're interested in using third party model, you could refer to https://mediaspace.esri.com/media/t/1_dke61ggy?st=2106   ... View more

@AviralPadlia The Roads Global model is updated and should work now. Could you please retry the workflw.   Please reach out if you are able to reproduce it. ... View more

Hello @Bartek_Sobieszek ,   Could you share what is the class value and class name within each folder of your training data? is it same or different? Since you have labelled penguins across all the folders.  ignore_classes won't work as it is designed to ignore certain classes while training when the number of classes are more than 2.  After understanding issue with your data classes, we could provide you a script that fixes your training data. ... View more

The arcgis.learn api has autodl class that provides a parameter named: network, which allows you to enter a a list of networks you'd like to evaluate.   @Sokna_Ly you could pass in networks, and omit yolov3 from this.   Additionally, in case you want us to investigate the issue with yolov3, please reach out to me @ptuteja@esri.com ... View more

@xingchenc Thanks for reporting the issue. We have logged this as a bug internally. Please expect the fix with Pro 3.3 release.   ... View more

https://developers.arcgis.com/python/guide/utilize-multiple-gpus-to-train-model/ ... View more

Dear @JadedEarth  The multi-gpu support for deep learning models listed here is available.  Please note: The multi-gpu training is supported with command line only. If you are trying to use ArcGIS Pro tools, it would not work. ... View more

Where have you deleted this class_0 from that it made the tool run successfully? ... View more

@Ha1 What is the version of arcgis python api installed? What command have you used for installation? If possible, also provide the video to reproduce the issue ... View more

Hi  @kaktuus  To run any arcgis.learn notebook in the VS Code, you need to ensure that you follow the following steps: Install Jupyter Extension (Use shortcut: Ctrl+Shift+X to open Extensions window-->Search Jupyter-->Install and enable) Set Python Interpreter as the Deep Learning Environment. Run your desired notebook. The output for Fit method should get visible. Alternatively, if you do not wish to be restricted to VS Code, you could launch Python Command Prompt or Anaconda Prompt (wherever you have created the deep learning environment). Activate the environment, and then type ‘jupyter notebook’. It would launch a localhost, from where you could run notebooks too. Now, the workflow for detecting trees on RGB images using MaskRCNN sounds like a good idea. I suggest you could also use AutoDL on your data (PascalVOC) and get a recommendation for the best performing Detection model. This can shorten your search criteria and may help provide better results that MaskRCNN. You will also need to set the cuda device for being able to train models on the GPU. In the first cell of your notebook, run the following: Import os os.environ[‘CUDA_VISIBLE_DEVICES’]=”0”   When fit method is running, you could open Task Manager and verify the Cuda GPU usage. For checking the Tensorboard, 'tensorboard --host=<machinename> --logdir="<datapath>\training_log"'               ... View more

@AlexDevoid Yes, the MultiTaskRoadExtractor model  works on multispectral imagery.  ... View more

@AdrienMichez  Please provide training data and your notebook so that i could try this on my end. Also, if possible, provide input imagery/labels for exporting the training data. Email address: ptuteja@esri.com ... View more

Hi @AdrienMichez    Could you please provide more information about your data? 1. What is the pixel depth of the input raster? 2. How does the images in data.show_batch look like? Any screenshot? 3. What error do you see when trying with ignore_classes=0.  We have a notebook that shows how to train a model with sparse data, https://developers.arcgis.com/python/samples/land-cover-classification-using-sparse-training-data/. What version of argis python api are you using? 4. How many features are labelled for class 1 and class 2 (info can be found in stat.txt file)? 5. How much time did you provide AutoDL to evaluate model performance on this data?     Additionally, there are some tips that you could try: 1. Try rescaling input raster to 8 bit unsigned and train model. 2. Use MMSegmentation based DeepLabV3Plus or HRNet for training. 3. USs PCA (Principal Component Analysis) tool in ArcGIS Pro and get most relevant bands to get the input raster. There may be bands adding noise to the data. 4. Try training with input which is not normalized or rescaled. Deep learning models do that internally so it may not be needed. However, just change the pixel depth of you composite raster. ... View more