BlenderProc VS segmentation_models

Blender with add-ons (Kubric, BlenderProc)

If you are interested, there are open source solutions on top of Unity, Blender, Unreal. You can generate yourself the data you described easier than it looks (the amount of options and settings can be intimidating with these tools).

Blender addons like https://github.com/google-research/kubric and https://github.com/DLR-RM/BlenderProc

I have been going through some papers and reviewing existing methods and I've come across stuff like UnrealCV (https://unrealcv.org/) and blenderproc (https://github.com/DLR-RM/BlenderProc).

I'm trying to use BlenderProc to automatically generate training data for object recognition.

Check this out https://github.com/DLR-RM/BlenderProc. I haven't used it extensively, but it seems to decent for generating synthetic image data.

That looks really interesting, have you seen BlenderProc: https://github.com/DLR-RM/BlenderProc it looks really similar just that BlenderProc already supports a vast variety of datasets and is fully documented.

Also, I’d suggest considering switching to the segmentation-models library - it provides U-Net models with a variety of pretrained backbones of as encoders. The author also put out a PyTorch version. https://github.com/qubvel/segmentation_models.pytorch https://github.com/qubvel/segmentation_models

I used segmentation-models (https://github.com/qubvel/segmentation_models) to create a deeplabv3+ model. I havent used it in the last 2 months and now i comeback to the same code and cant use it. Getting ModuleNotFoundError: No module named 'segmentation_models_pytorch.deeplabv3'

I'm building some semantic segmentation models off of low-moderate volumes of biomedical images (~500 - 1k images). So far I've done some hyperparameter sweeping (learning rate, transfer learning, architectures, dropout layers) using the Segmentation Models package from qubvel https://github.com/qubvel/segmentation_models but I'm only seeing moderate performance and minimal differences between tested parameters.

So, image segmentation (especially for satellite images) is a known problem. Search for semantic segmentation and unet (a model used for semantic segmentation). Also, if you use tensorflow there is this library which I found useful segmentation models.

I am doing an image segmentation project using https://github.com/qubvel/segmentation_models as the baseline. I was wondering if any of you have tried the latest segmentation models from cvpr papers. If yes, which ones you found to be interesting or actually improve miou. And how difficult/easy it is to implement those?

That is just U-net from this repo, anything aside is slicing images to fit into window and predict call. I measure time of predict() and it is the same as profiler numbers, so definitely my other operations are beyond profiler. C API code is just creating tensors and calling TF_SessionRun plus slice operations with opencv. Can't post code, sorry.

I'm trying to train image segmentation model with transfer learning using https://github.com/qubvel/segmentation_models/.