rocauc

7 ms·

1.
by rocauc 1mo ago
thank you for the model suggestions!
2.
Audio is the one area small labs are winning(amplifypartners.com)
319 points by rocauc 7mo ago|96 comments
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Proof of Corn(proofofcorn.com)
476 points by rocauc 8mo ago|307 comments
4.
by rocauc 10mo ago
As someone that works on a platform users have used for labeling 1B images, I'm bullish SAM 3 can automate at least 90% of the work. Data prep is flipped to models being human-assisted instead of humans being model-assisted (see "
5.
by rocauc 10mo ago
A brief history. SAM 1 - Visual prompt to create pixel-perfect masks in an image. No video. No class names. No open vocabulary. SAM 2 - Visual prompting for tracking on images and video. No open vocab. SAM 3 - Open vocab concept segmentatio
6.
by rocauc 10mo ago
The model supports batch inference, so all prompts are sent to the model, and we parse the results.
7.
by rocauc 10mo ago
I tried it on transparent glass mugs, and it does pretty well. At least better than other available models: https://i.imgur.com/OBfx9JY.png Curious if you find interesting results - https://playground.roboflow.co
8.
by rocauc 10mo ago
Yes. But also note that redistribution of SAM 3 requires using the same SAM 3 license downstream. So libraries that attempt to, e.g., relicense the model as AGPL are non-compliant.
9.
by rocauc 10mo ago
Yes. It's a custom license with an Acceptable Use Policy preventing military use and export restrictions. The custom license permits commercial use.
10.
by rocauc 10mo ago
yes, downdetectorsdowndetectorsdowndetectorsdowndetector is available.
11.
by rocauc 11mo ago
The bike lane compliant vehicle category is exciting. Infinite Machine (infinitemachine.com) made me aware of this category with their Olto model, which is at a (surprisingly) superior price point.
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by rocauc 11mo ago
Not nearly enough gradient for a vibe coded site :)
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by rocauc 11mo ago
One of the most common uses for edge AI not listed in this course is computer vision. You similarly want real-time inference for processing video. Another open source project that makes it easy to use SOTA vision models on the edge is infer
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by rocauc 1y ago
Reminds me of NY Cerebro, semantic search across New York City's hundreds of public street cameras: https://nycerebro.vercel.app/ (e.g. search for "scaffolding")
15.
by rocauc 1y ago
both the endeavor and the site are super cool - congrats on 10 years. interaction on the graphics would be a nice touch to select into a specific run. went looking for the code on your GH! https://github.com/friggeri
16.
by rocauc 2y ago
In the 2019 fatal Tesla Autopilot crash, the Tesla failed to identify a white tractor trailer crossing the highway: https://www.washingtonpost.com/technology/interactive/2023/t...
17.
by rocauc 2y ago
I wonder how long until techniques like Depth Anything ( https://depth-anything-v2.github.io/ ) provide parity with human depth perception. In Mark Rober's tests, I'm not sure even a human would have passed the fog
18.
by rocauc 2y ago
Meta deeply comprehends the impact of GPT-3 vs ChatGPT. The model is a starting point, and the UX of what you do with the model showcases intelligence. This is especially pronounced in visual models. Telling me SAM2 can "see anything&q
19.
by rocauc 2y ago
A suggestion: I'd swap llava for Florence-2 for your open set text description. Florence-2 seems uniformly more descriptive in its outputs.
20.
by rocauc 2y ago
SAM 2's key contribution is adding time-based segmentation to apply to videos. Even on images alone, the authors note [0] the image-based segmentation benchmark does exceed SAM 1 performance. There have been some weaknesses exposed in
21.
by rocauc 2y ago
One thing its enabled is automated annotations for segmentation, even on out-of-distribution examples. e.g. in the first 7 months of SAM, users on Roboflow used SAM-powered labeling to label over 13 million images, saving over ~21 years[0]
22.
by rocauc 2y ago
"Load Example" was very helpful to get a sense of what this does. Awesome build. +1 to the other comment wanting a breakdown of what the colors mean. Also, combining this with real-time in-game camera play could be really powerful
23.
by rocauc 2y ago
i work on roboflow. seeing all the creative ways people use computer vision is motivating for us. let me know (email in bio) if there's things you'd like to be better.
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Florence-2: MIT Open Source Vision Foundation Model by Microsoft(blog.roboflow.com)
4 points by rocauc 2y ago|0 comments
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CoreNet: A library for training deep neural networks(github.com)
494 points by rocauc 2y ago|131 comments
26.
by rocauc 2y ago
Pulling out a key part of this post from a DeepMind 2023 paper[1]: “Although the success of ViTs in computer vision is extremely impressive, in our view there is no strong evidence to suggest that pre-trained ViTs outperform pre-trained Con
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by rocauc 3y ago
suno has improved fast. I remember when they released Bark in April ‘23. it was good. but this new model is fun . props to the team.
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by rocauc 3y ago
Really neat. I tried your search for red shoes, and I found some, er, unexpected imagery on page 1. One thing you could do is add semantic search so when a user searches "red shoes," the index returns images that look like red sho
29.
by rocauc 3y ago
In your inference project example, what examples do you place in Getting Started vs common usage examples? In general, where is the best place for usage examples - alongside the methods they use, or in an independent section?
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Show HN: OpenAI GPT Vision Experiments(github.com)
3 points by rocauc 3y ago|0 comments

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