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facebookresearch
detectron2
Python2019–2026
facebookresearch / detectron2

detectron2

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

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12Distinct people
12Mentions
0Explicit recommenders
35KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

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No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:01:16 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year12 dated mentions

Observed mentions: 2019-10-11 03:06:59 UTC to 2026-02-04 14:14:46 UTC.

01232019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC312
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC202
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC202
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC101
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC101
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC202
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC110

What Hacker News says

2 sources
TaylorAlexander·May 3, 2020
I have one of these powering my open source four wheel drive robot. [1] I've started doing machine learning experiments with it finally. (See [1] for details) There's a few tricks to getting the best performance. You want to convert your neural network to run with NVIDIA's TensorRT library instead of just tensorflow or torch. TensorRT does all the optimized goodness that gets you the most out of the hardware. Not all possible network operations can run in TensorRT (though nvidia updates the framework regularly). This means some networks can't be easily converted to something fully optimized for this platform. Facebook's detectron2 for example uses some operations that don't readily convert. [2] But then if you're new like me you've got to both find some code that will ultimately produce something you can convert to TensorRT, and you also
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need something that you can easily train. I've learned that training using your own dataset is often non-obvious. A lot of example code shows how to use an existing dataset but they totally gloss over the specific label format those datasets use. That means you've got to do some digging to figure out how to make your own dataset load properly in to the training code. After trying a few different things, I've gotten some good results training using Bonnet(al) [3]. I was able to make enough sense of its training code to use my own dataset, and it looks like it will readily convert to TensorRT. Then you load the converted network using NVIDIA's Deepstream library for maximum pipeline efficiency [4]. The performance numbers for the AGX Xavier are very good, and I am hopeful I will get my application fully operation soon enough. [1] https://reboot.love/t/new-cameras-on-rover/ [2] https://github.com/facebookresearch/detectron2/issues/192 [3] https://github.com/PRBonn/bonnetal [4] https://developer.nvidia.com/deepstream-sdk
In Jetson AGX Xavier

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