11/13/2023 0 Comments Nn models images![]() ![]() Importing all the required packages: # Import packages import neptune Yes, Neptune can well be used to track image processing models! In this blog, I will also be demonstrating how to leverage Neptune during the image segmentation implementation. What’s cool about experiment tracking with Neptune is that it will automatically generate performance charts for practitioners to compare different runs, and thus to select an optimal one.įor a more detailed explanation of configuring your Neptune environment and setting up your experiment, please check out this complete guide and my other blog here on Implementing macro F1 scores in Keras. When it comes to the model training process, Neptune offers an effective yet easy-to-use way to track and log almost everything model-related, from hyperparameters specification to best model saving, to result from plots logging and so much more. Image segmentation model tracking with Neptune Next, from the Mask_RCNN project Github, let’s download the model weights into the current working directory: mask_rcnn_coco.h5 For Windows users, if you are asked to install the pycocotools, be sure to use pip install pycocotools-windows, rather than the pycocotools as it may have compatibility issues with Windows.If you encounter this error message: ZipImportError: bad local file header: mask_rcnn-2.1-p圓.7.egg.Run: python setup.py clean -all install.Navigate to the folder containing the repo.This will create a new folder named “updated_mask_rcnn” to differentiate the updated version from the original one. Tensorflow 1+ and keras prior to 2.2.4:.Therefore, please make sure to clone the correct repo according to your Tensorflow versions. If you prefer not to customize your code, an updated version for Tensorflow 2+ is also available here. Hence, for all Tensorflow 2+ users, myself included, getting it to work becomes quite challenging as it would require significant modifications to the source code. One caveat here is that the original Matterport code has not been updated to be compatible with Tensorflow 2+. Since there is no distributed version of this package so far, I put together several steps to install it by cloning from the Github repo: For this exercise, the algorithm implementation by Matterport will be used. ![]() Specifically, we will utilize the weights of the Mask R-CNN model pretrained on the COCO dataset aforementioned to build an inference type of model.ĭuring the model building process, we will also set up Neptune experiments to track and compare prediction performance with different hyperparameter tuning.įirst and foremost, we need to install the required packages and set up our environment. Mask R-CNN use casesįor today’s introductory use case demonstration, we will be focusing on the Mask R-CNN framework for image segmentation. Now, let’s work on a couple of Mask R-CNN use cases to automatically segment and construct pixel-wise masks for each object in an image. ![]()
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