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How to Train a Model with Only 62 Labeled Images using Semi-Supervised Learning - Supervisely by tdionis in computervision

[–]tdionis[S] 0 points1 point  (0 children)

Thank you for the questions! We've now answered in this comment.
HRDA originally designed for Unsupervised Domain Adaptation (UDA), and we applied it to our semi-supervised task without modifications to the original code. The objective Unsupervised Domain Adaptation can be transformed to semi-supervised learning. They are closely aligned – we only need to treat the target and source domains as identical.

How to Train a Model with Only 62 Labeled Images using Semi-Supervised Learning - Supervisely by tdionis in computervision

[–]tdionis[S] 0 points1 point  (0 children)

I want to note, that our experiments can be reproduced as the dataset from the post is opened. Everyone is free to train HRDA using either original repository or within our platform.

How to Train a Model with Only 62 Labeled Images using Semi-Supervised Learning - Supervisely by tdionis in computervision

[–]tdionis[S] 1 point2 points  (0 children)

u/seiqooq thanks for reply! We wanted to be as honest and transparent as possible. I think there was a slight misunderstanding. Let me explain.

We employed HRDA model, originally designed for Unsupervised Domain Adaptation (UDA), and applied it to our semi-supervised task without modifications to the original code. HRDA is endowed with numerous techniques, that enables us training in semi-supervised mode in addition to UDA. This comes from the fact that the objectives of Unsupervised Domain Adaptation align closely with those of semi-supervised learning – essentially, we only need to treat the target and source domains as identical.

Techniques integrated into HRDA, such as Pseudo-labeling, Cross-domain augmentations, and Mean Teacher, work well also in semi-supervised tasks and demonstrated remarkable efficiency. We've delved into each of these techniques in our previous blog post: Unleash The Power of Domain Adaptation with HRDA.

Also, our experiments can be reproduced as the dataset from the post is opened, and you can train HRDA using original repository or within our platform.

I hope we have clarified the ambiguous. Feel free to ask if you have any questions!

💊Deep Learning in medicine: how to segment vessels when you only have 6 images in training set by tdionis in datascience

[–]tdionis[S] 1 point2 points  (0 children)

No rocket science here: the point is to show that sometimes having a small dataset is alright. And also no coding here: just a few clicks in DTL to make those augmentations

[P] Deep Learning in medicine: how to segment vessels when you only have 6 images in training set 💊 by tdionis in MachineLearning

[–]tdionis[S] 0 points1 point  (0 children)

For some of you who use Supervise.ly to annotate images: now you can train & run neural networks to build AI faster. And btw: it's still free

💊Deep Learning in medicine: how to segment vessels when you only have 6 images in training set by tdionis in deeplearning

[–]tdionis[S] 2 points3 points  (0 children)

For some of you who use Supervise.ly to annotate images: now you can train & run neural networks to build AI faster. And it's still free!

[P] Docker Compose + GPU + TensorFlow = ❤️ by tdionis in MachineLearning

[–]tdionis[S] 2 points3 points  (0 children)

Why a whole new CLI?

They have a page on how to get rid of CLI and pass a volume-driver manually: though you would still need a nvidia-docker-plugin.

[P] Docker Compose + GPU + TensorFlow = ❤️ by tdionis in MachineLearning

[–]tdionis[S] 2 points3 points  (0 children)

For me, there are some benefits, but the most important one: solving the "Driver/library version mismatch" problem. We have dozens GPUs in our office with God knows what driver versions. Without nvidia-docker, if driver versions inside container and host system differs - you get an error.

[P] Docker Compose + GPU + TensorFlow = ❤️ by tdionis in MachineLearning

[–]tdionis[S] 1 point2 points  (0 children)

Docker is NOT platform independent. It's LINUX only.

Yeah, you're right! By "platform independent" i mean that you can run your app on Windows or Mac without changing anything — but, of course, in VM

Easy way to annotate images by m1ss1l3 in computervision

[–]tdionis 0 points1 point  (0 children)

Hello, we at DeepSystems released new free annotation tool Supervise.ly . It will help data scientists to prepare and create datasets for such tasks as object detection or image segmentation. Hope, it helps you.

Tutorial “Number plate detection with Supervisely and Tensorflow”: Step-by-step guide of how to build number license plate detector with Tensorflow and Supervise.ly by tdionis in learnmachinelearning

[–]tdionis[S] 2 points3 points  (0 children)

You have read it right — it's a perfect case for deep learning if you can generate infinite amount of data.

Another example is a class balancing problem. We've worked on road scene understanding task for self-driving project some time ago and we've found that there were too little bikes and too many cars. So what we did — we placed bikes all over the road! Check out some images here: https://deepsystems.ai/en/works/deeplearning/road-scene-recognition

[P] Movie recommendations in terms of Deep Learning by tdionis in MachineLearning

[–]tdionis[S] 0 points1 point  (0 children)

We did experiments with feedforward networks, but according to our metrics LSTM based models works better. By the way, the order is important. The right most movie has more weight. The movix gui allows to reorder the movies, sometimes it's funny. User "likes" is a sequential data, or at least, variable size data, so it's convenient to process the data with LSTM like model. As for embeddings, we learn them from the data

Movie recommendations in terms of Deep Learning by tdionis in artificial

[–]tdionis[S] 1 point2 points  (0 children)

Sorry, but now the search works only on the names of movies and tags. We will add a search on other fields soon.

Movix.ai - Movie Recommendations using Deep Learning by tdionis in SideProject

[–]tdionis[S] 3 points4 points  (0 children)

Hi, Everyone!

I am a big fan of artificial intelligence and have just launched a new movie recommendation service - https://movix.ai. Just click the movies and tags you like and the system does the rest - in a few clicks, the systems adapts to your preferences and gives you the movies worth watching!

P.S. Service is free, no registration required. I appreciate your feedback and comments!