Ciro Santilli's raison d'etre, one of his attempts: OurBigBook.com.
The outcome of closed knowledge is reverse engineering.
Full name Carl Mark Force IV, the fourth! As mentioned at www.vice.com/en/article/vv7dgj/great-moments-in-shaun-bridges-a-corrupt-silk-road-investigator (this made Ciro Santilli laugh quite hard:
Carl Mark Force IV - the other corrupt cop charged alongside Bridges - is pretty hard to beat, just name-wise.
The main FeathersJS hello world demo. Notable missing things...
- instant Heroku deployability: FeathersJS Heroku deployment
- no Front-end web framework which sucks, but there are basically official demos that worked e.g. feathers-chat-react
- FeathersJS signup email verification
ImageNet Large Scale Visual Recognition Challenge dataset Updated 2025-01-10 +Created 1970-01-01
Subset of ImageNet. About 167.62 GB in size according to www.kaggle.com/competitions/imagenet-object-localization-challenge/data.
Contains 1,281,167 images and exactly 1k categories which is why this dataset is also known as ImageNet1k: datascience.stackexchange.com/questions/47458/what-is-the-difference-between-imagenet-and-imagenet1k-how-to-download-it
www.kaggle.com/competitions/imagenet-object-localization-challenge/overview clarifies a bit further how the categories are inter-related according to WordNet relationships:
The 1000 object categories contain both internal nodes and leaf nodes of ImageNet, but do not overlap with each other.
image-net.org/challenges/LSVRC/2012/browse-synsets.php lists all 1k labels with their WordNet IDs.There is a bug on that page however towards the middle:and there is one missing label if we ignore that dummy
n02119789: kit fox, Vulpes macrotis
n02100735: English setter
n02096294: Australian terriern03255030: dumbbell
href="ht:
n02102040: English springer, English springer spanielhref= line. A thinkg of beauty!Also the lines are not sorted by synset, if we do then the first three lines are:
n01440764: tench, Tinca tinca
n01443537: goldfish, Carassius auratus
n01484850: great white shark, white shark, man-eater, man-eating shark, Carcharodon carchariasgist.github.com/aaronpolhamus/964a4411c0906315deb9f4a3723aac57 has lines of type:therefore numbered on the exact same order as image-net.org/challenges/LSVRC/2012/browse-synsets.php
n02119789 1 kit_fox
n02100735 2 English_setter
n02110185 3 Siberian_huskygist.github.com/yrevar/942d3a0ac09ec9e5eb3a lists all 1k labels as a plaintext file with their benchmark IDs.therefore numbered on sorted order of image-net.org/challenges/LSVRC/2012/browse-synsets.php
{0: 'tench, Tinca tinca',
1: 'goldfish, Carassius auratus',
2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',The official line numbering in-benchmark-data can be seen at
LOC_synset_mapping.txt, e.g. www.kaggle.com/competitions/imagenet-object-localization-challenge/data?select=LOC_synset_mapping.txtn01440764 tench, Tinca tinca
n01443537 goldfish, Carassius auratus
n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carchariashuggingface.co/datasets/imagenet-1k also has some useful metrics on the split:
- train: 1,281,167 images, 145.7 GB zipped
- validation: 50,000 images, 6.67 GB zipped
- test: 100,000 images, 13.5 GB zipped
A more specific type of E-learning website generally run by a specific organization.
A website, usually hosted by an university, that takes what is done in class, and pastes it online. It is already much more rational and efficient, and opens up the way for potential sharing outside of the institution (or by default paywalling as the University of Oxford did.
The fundametnal problem with VLEs is that they tend to not have enough incentives for students to contribute at all to the content. This is basically the major motivation behind OurBigBook.com.
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