comma10k
lightly
comma10k | lightly | |
---|---|---|
9 | 16 | |
657 | 2,750 | |
0.5% | 1.1% | |
8.6 | 8.8 | |
7 days ago | 9 days ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
comma10k
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Are there ways to contribute to openpilot, other than chffr or commacoloring, that are now discontinued ?
Comma pencil is still a thing that needs lots of help. https://github.com/commaai/comma10k Also, sure read the OpenPilot code, find bugs or improvements and submit useful PRs.
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commacoloring.com down ?
The modern successor is https://github.com/commaai/comma10k
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openpilot 0.8.14: Big model now live!
You can see the raw data that Comma is using for both cameras here: https://github.com/commaai/comma10k/tree/master/imgs2
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TRY A TWO! Comma Pencil, End to End Longitudinal Demo, Mazda Brand Port & More
TRY A TWO! https://comma.ai/compare Help with comma pencil! https://github.com/commaai/comma10k
- [Discussion] Is there a way I can volunteer/contribute to research datasets?
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Open-Source Autonomous Driving Datasets
There's also comma10k, which is actually more in the spirit of open source relying on open collaboration. https://github.com/commaai/comma10k
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Comma Ai's Harald Schäfer – Building a Super Human Driving Agent @ COMMA_CON
Openpilot is not fully end to end to yet. The longitudinal policy is still done classically, which requires explicit lead car detection. And by default the lateral does explicit lane line detection to keep itself centered with a hand coded planer. I can't comment on exactly how it's done since my understand of machine learning is purely superficial, but the model is also trained to detect lane lines and lead cars using a segnet based on this comma10k dataset, which has hand labeled images. Sky isn't one of these labels though.
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Show HN: Cell Shield – shields.io badge from a cell in a public Google sheet
This is great for projects where management or tracking of some sort is done from a public Google spreadsheet. Here's some examples where I've put it to use.
Here's a project where I sent in a PR that was merged to display annotation progress:
https://github.com/commaai/comma10k/pull/2767
Here's a wiki entry using it to display the current number of bounty contributors and bounty amount ($8K+!):
https://github.com/commaai/openpilot/wiki/Toyota-Lexus#2021-...
If you're organizing something for your community using a spreadsheet, maybe consider using Cell Shield to put a little badge somewhere visible.
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Active Learning using Detectron2
Our goal is to use active learning to use a COCO pre-trained model and fine-tune it on a dataset for autonomous driving. For this transfer task, we are using the Comma10k dataset. From the repository: "It's 10,000 PNGs of real driving captured from the comma fleet. It's MIT license, no academic-only restrictions or anything."
lightly
- Show HN: Lightly – A Python library for self-supervised learning on images
- GitHub - lightly-ai/lightly: A python library for self-supervised learning on images.
- A Python library for self-supervised learning on images
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[P] Release of lightly 1.2.39 - A python library for self-supervised learning
Another year of has passed, and we’ve seen exciting progress in research around self-supervised learning in computer vision. We’re very excited that some of the recent models such as Masked Autoencoders (MAE) or Masked Siamese Networks (MSN) have been added to our OSS framework.
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Self-Supervised Models are More Robust and Fair
If you’re interested in self-supervised learning and want to try it out yourself you can check out our open-source repository for self-supervised learning.
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[D] Can a Siamese Neural Network work for invoice classification?
I assume that you have an image of the invoice. Then using a framework like https://github.com/lightly-ai/lightly with many implemented algorithms is the way to go. And after that step, with model-producing embeddings, you need to compare the embedding of a query with your known database and check if the distance is below some threshold. Of course, pipeline with checking the closest neighbor can be more complicated but I would start with sth really simple.
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[P] TensorFlow Similarity now self-supervised training
https://github.com/lightly-ai/lightly implements a lot of self supervised models, and had been available for a while.
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Launch HN: Lightly (YC S21): Label only the data which improves your ML model
modAL indeed has a similar goal of choosing the best subset of data to be labeled. However it has some notable differences:
modAL is built on scikit-learn which is also evident from the suggested workflow. Lightly on the other hand was specifically built for deep learning applications supporting active learning for classification but also object detection and semantic segmentation.
modAL provides uncertainty-based active learning. However, it has been shown that uncertainty-based AL fails at batch-wise AL for vision datasets and CNNs, see https://arxiv.org/abs/1708.00489. Furthermore it only works with an initially trained model and thus labeled dataset. Lightly offers self-supervised learning to learn high dimensional embeddings through its open-source package https://github.com/lightly-ai/lightly. They can be used through our API to choose a diverse subset. Optionally, this sampling can be combined with uncertainty-based AL.
- Lightly – A Python library for self-supervised learning on images
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Active Learning using Detectron2
You can easily train, embed, and upload a dataset using the lightly Python package. First, we need to install the package. We recommend using pip for this. Make sure you're in a Python3.6+ environment. If you're on Windows you should create a conda environment.
What are some alternatives?
openpilot - openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for 250+ supported car makes and models.
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
autoware - Autoware - the world's leading open-source software project for autonomous driving
simsiam-cifar10 - Code to train the SimSiam model on cifar10 using PyTorch
byol - Implementation of the BYOL paper.
dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
byol-pytorch - Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch
DataProfiler - What's in your data? Extract schema, statistics and entities from datasets
Transformer-SSL - This is an official implementation for "Self-Supervised Learning with Swin Transformers".
Ne2Ne-Image-Denoising - Deep Unsupervised Image Denoising, based on Neighbour2Neighbour training
modAL - A modular active learning framework for Python
EasyCV - An all-in-one toolkit for computer vision