94 Interest Score
9 Discussions
0.10 Engagement
Jul 2021 Launched

Portal is an open-source visual inspection tool for vision models 🔎 No more wrangling with cv2 or matplotlib codes to visualize or test your models, especially on videos. Portal is the fastest way to inspect your neural network inferences on your datasets! 🎉

What the Community Said

Hey ProductHunt ✌🏻 Ever wondered if there's a better way to visualize and discuss neural network results apart from Jupyter Notebooks or using cv2? 🔮 Portal is the easiest way to visualize neural network inferences. For ML engineers and data teams, sharing visual results is challenging and time-consuming as people would run cv2 or matplotlib over and over in Jupyter Notebooks just to visualize the results 😿 🕸 So we made a web-based application to do this! This way, users can adjust confidence th

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I have just tried using Portal to perform some inferences for one of my object-detection projects and it worked great! All I had to do was register my TensorFlow model, add some images, load the model, and leave the rest to Portal - all without writing any code 👍🏼 I was even able to filter my predictions on a video while the video is playing, which is a huge plus, saving me a lot of time in the process. Looking forward to future updates and developments!

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I was part of Portal's pre-release testing and it really blew me away. As an avid Computer Vision enthusiast, I have always found it a hassle to write code from scratch to visualise predictions every time I explore a new model. Being able to seamlessly load models from different architectures and play with the predictions is such a convenience and time-saver! One bonus I've noticed is the ability to navigate freely between video frames without any stutter. Performance is also blazing fast even w

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The program ran smoothly for me. Coming from someone who understands the amount of code needed to be written for a single machine learning project, this can be considered as a potential game changer in the industry. I’ll continue to watch the development of this product, and since it is open source I may also contribute to its development.

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Frequently Asked Questions

Discussion threads divided by interest score. Above 0.30 is strong. Below 0.15 suggests the product got clicks but not conversation.

Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.

The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.

Check the similar products section on this page, or browse the category pages linked in the tags above. Each category page shows all products for a given year, sorted by engagement.

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