image-tools VS tfjs

Also here is a desktop app that uses this library to show GradCAM++ heatmaps for a selected image on your filesystem: https://github.com/lobe/image-tools

You could also use our Image Tools helper app to export your dataset into the folder structure based on the labels, and drag it back into Lobe to recreate your project from the same data source. Note that this will require training from scratch again: https://github.com/lobe/image-tools

My team is currently working on a project to find animal abundance using hundreds of game cameras placed out in the woods. Our plan was to run the image folders from each camera (about 10k pics per camera) using the image tools desktop app (https://github.com/lobe/image-tools). After compiling 500k+ training data sets and building a model to recognize camera operating states in lobe, I noticed the image tools artifact link is expired and hasn't been updated in a while. Is there a new app or link to run large image folders locally? Are you guys working on something else that fills that role? My team is made up of wildlife biologists not programmers so any advice would be appreciated. Thanks!

Hi! You can export images with their labels as folder names using our Image Tools utility: https://github.com/lobe/image-tools (for Mac you will need to either build the GUI app locally or use the command line).

I also have an example app with PyQt using Python: https://github.com/lobe/image-tools

Yes! We have a GitHub project called image-tools that let's you use a Lobe model and make predictions on thousands of photos from and to CSV or XLSX. Here is a link to the GitHub: https://github.com/lobe/image-tools

The easiest way would be to share the dataset and have your friend import it and train their own model. If your images are labeled inside of Lobe and not already put in folders on your computer, you can use the python command line helper here to export your images into a labeled format: https://github.com/lobe/image-tools#export-lobe-dataset

Hi! We are working to make this a lot easier at the moment. Where do your images live and how would you like to use this classification? Right now, you can check out our image-tools helper app that can classify images from web or file url's in a spreadsheet (https://github.com/lobe/image-tools). Additionally, I'm planning to give you the option in image-tools to sort folders on your filesystem based on the prediction of the Lobe model (it is possible through the command line python usage, but I'm going to add it to the app section of the tool for no-code).

If you are using the export your images from Lobe, that is using shutil.copyfile with our stored blob (https://github.com/lobe/image-tools/blob/master/dataset/export_from_lobe.py#L103). I believe it works across image file types because we will convert to jpg, but what were your original file formats?

We don't have a way of manually adding test images at the moment, but Lobe will keep an 80% train / 20% test split randomly from the data you import. It should be having 4 test images if you have 20 overall. If you want to use an outside folder as a test set of images, check out the image-tools repo we have that can help run an exported Lobe model and organize images into a folder structure based on the predicted label: https://github.com/lobe/image-tools#folder-of-images

TensorFlow.js

Many people seem to be unaware of tensorflow.js, an official JS implementation of TF

https://github.com/tensorflow/tfjs

I'd love to see PyTorch in JS, but I think unless you get it running on the GPU it won't be able to do much.

TensorflowJS GitHub Repository

Also, Tensorflow.js WebGPU backend has been in the works for quite some time: https://github.com/tensorflow/tfjs/tree/master/tfjs-backend-...

It's a pity that tfjs never truly developed any decent ops. E.g. you need lgamma to implement the cap for zero-inflated poisson regression and tfjs simply doesn't have that: https://github.com/tensorflow/tfjs/issues/2011

People have been doing it for long with WebGL, see eg https://github.com/tensorflow/tfjs and https://cloudblogs.microsoft.com/opensource/2021/09/02/onnx-...

thanks, no not unity, going to show it as a demo with threejs + tensorflow on the web. I found a github request to add face orientation https://github.com/tensorflow/tfjs/issues/3835 looks like they assigned someone to add it but doesn't look like its available yet, but there's some posts about the math I can use to get rotations based on some of the landmarks

There already exist a wide variety of neural network inference engines that run in web browsers (e.g. TensorFlow.js and, my personal favorite for use with PyTorch models, ONNX Runtime Web), but pre- and post-processing has always required imperative manipulations on flat buffers rather than a clean ndarray interface.

Full docs and code: https://github.com/tensorflow/tfjs/tree/master/e2e/benchmarks/local-benchmark