Was a closed source project by "Roboti LLC", which was then acquired by DeepMind in October 2021 and open sourced March 2022: www.deepmind.com/blog/open-sourcing-mujoco
The most minimal hello world with gnuplot visualization!!! stackoverflow.com/questions/11175694/bullet-physics-simplest-collision-example/36987063#36987063
The official page: www.image-net.org/challenges/LSVRC/index.php points to a download link on Kaggle: www.kaggle.com/competitions/imagenet-object-localization-challenge/data Kaggle says that the size is 167.62 GB!
To download from Kaggle, create an API token on kaggle.com, which downloads a The download speed is wildly server/limited and take A LOT of hours. Also, the tool does not seem able to pick up where you stopped last time.
kaggle.json file then:mkdir -p ~/.kaggle
mv ~/down/kaggle.json ~/.kaggle
python3 -m pip install kaggle
kaggle competitions download -c imagenet-object-localization-challengeAnother download location appears to be: huggingface.co/datasets/imagenet-1k on Hugging Face, but you have to login due to their license terms. Once you login you have a very basic data explorer available: huggingface.co/datasets/imagenet-1k/viewer/default/train.
Subset generators:
- github.com/mf1024/ImageNet-datasets-downloader generates on download, very good. As per github.com/mf1024/ImageNet-Datasets-Downloader/issues/14 counts go over the limit due to bad multithreading. Also unfortunately it does not start with a subset of 1k.
- github.com/BenediktAlkin/ImageNetSubsetGenerator
Unfortunately, since ImageNet is a closed standard no one can upload such pre-made subsets, forcing everybody to download the full dataset, in ImageNet1k, which is huge!
By cranks:
- www.thehighestofthemountains.com/ has some diagrams. It is unclear how they were obtained, except that they were made over the course of 5 years by a "Space Shuttle Engineer", classic crank appeal to authority. The author belives that brain function is evidence of intelligent design.
This is a quick presentation that goes over some of the most common difficulties people find with Git.
The fundamental insight of Git design is: a SHA represents not only current state, but also the full history due to the Merkle tree implementation, see notably:
This makes it so that you will always notice if you are overwriting history on the remote, even if you are developing from two separate local computers (or more commonly, two people in two different local computers) and therefore will never lose any work accidentally.
It is very hard to achieve that without the Merkle tree.
Consider for example the most naive approach possible of marking versions with consecutive numbers:
There are unlisted articles, also show them or only show them.