OMG this is sick.
Full official album on YouTube: www.youtube.com/playlist?list=OLAK5uy_myUBkJ0UJbkV7O1q-hg7ONxxGBLGz4SzU. so glad he managed to upload it...
This is the kind of mind-bending argument twists that Ciro Santilli loves. If we take out the "a real child is hurt" aspect, does it still make sense to make it illegal?
Of course, hentai of young-looking girls has already existed since forever. Bit with newer image generation methods in the 2020s used for computer generated child pornography, especially sometimes photorealistic text-to-image generation, things are taken to another level.
- digitalcommons.law.seattleu.edu/cgi/viewcontent.cgi?article=1585&context=sulr
- www.inverse.com/article/12948-computer-generated-vr-child-pornography-will-be-horrible-illegal-and-immersive
- www.crime-research.org/library/Illegal1.htm
- www.quora.com/Should-purely-computer-generated-imagery-CGI-of-child-pornography-be-illegal
This is a porn style defined by Ciro Santilli as:
Ciro believes that this is an interesting type of pornography, as it feels more natural and humane than all the horrible trash that comes out of horrendous professional mainstream porn industry.
Yes, it could go down the YouTube/Instagram alley, and lead the vloggers to do things they wouldn't normally do because of the audience. But who is to say that Ciro Santilli doesn't do the same on Stack Overflow to some extent?
That type of porn requires some big courage to make. Or balls if you will. Kudos to those creators, as it is so taboo it could greatly impact their future job prospects.
The travel sex vlog appears to be the most popular way to do it. Presumably the reason being that you would not be able to interact with people in a normal job, so to keep things interesting you need to go to some random places.
Examples:
- twitter.com/JamesWithLola also a travel sex vlog, also French. Hum.
pytorch.org/vision/0.13/models.html has a minimal runnable example adapted to python/pytorch/resnet_demo.py.
That example uses a ResNet pre-trained on the COCO dataset to do some inference, tested on Ubuntu 22.10:This first downloads the model, which is currently 167 MB.
cd python/pytorch
wget -O resnet_demo_in.jpg https://upload.wikimedia.org/wikipedia/commons/thumb/6/60/Rooster_portrait2.jpg/400px-Rooster_portrait2.jpg
./resnet_demo.py resnet_demo_in.jpg resnet_demo_out.jpgWe know it is COCO because of the docs: pytorch.org/vision/0.13/models/generated/torchvision.models.detection.fasterrcnn_resnet50_fpn_v2.html which explains that is an alias for:
FasterRCNN_ResNet50_FPN_V2_Weights.DEFAULTFasterRCNN_ResNet50_FPN_V2_Weights.COCO_V1After it finishes, the program prints the recognized classes:so we get the expected
['bird', 'banana']bird, but also the more intriguing banana.
Next, in the and then let's create the then back on the mlperf directory we download our model:and finally run!which gives on P51:where The
imagenette2 directory, first let's create a 224x224 scaled version of the inputs as required by the benchmark at mlcommons.org/en/inference-datacenter-21/:#!/usr/bin/env bash
rm -rf val224x224
mkdir -p val224x224
for syndir in val/*: do
syn="$(dirname $syndir)"
for img in "$syndir"/*; do
convert "$img" -resize 224x224 "val224x224/$syn/$(basename "$img")"
done
doneval_map.txt file to match the format expected by MLPerf:#!/usr/bin/env bash
wget https://gist.githubusercontent.com/aaronpolhamus/964a4411c0906315deb9f4a3723aac57/raw/aa66dd9dbf6b56649fa3fab83659b2acbf3cbfd1/map_clsloc.txt
i=0
rm -f val_map.txt
while IFS="" read -r p || [ -n "$p" ]; do
synset="$(printf '%s\n' "$p" | cut -d ' ' -f1)"
if [ -d "val224x224/$synset" ]; then
for f in "val224x224/$synset/"*; do
echo "$f $i" >> val_map.txt
done
fi
i=$((i + 1))
done < <( sort map_clsloc.txt )wget https://zenodo.org/record/4735647/files/resnet50_v1.onnxDATA_DIR=/mnt/sda3/data/imagenet/imagenette2 time ./run_local.sh onnxruntime resnet50 cpu --accuracyTestScenario.SingleStream qps=164.06, mean=0.0267, time=23.924, acc=87.134%, queries=3925, tiles=50.0:0.0264,80.0:0.0275,90.0:0.0287,95.0:0.0306,99.0:0.0401,99.9:0.0464qps presumably means "querries per second". And the time results:446.78user 33.97system 2:47.51elapsed 286%CPU (0avgtext+0avgdata 964728maxresident)ktime=23.924 is much smaller than the time executable because of some lengthy pre-loading (TODO not sure what that means) that gets done every time:INFO:imagenet:loaded 3925 images, cache=0, took=52.6sec
INFO:main:starting TestScenario.SingleStreamLet's try on the GPU now:which gives:TODO lower
DATA_DIR=/mnt/sda3/data/imagenet/imagenette2 time ./run_local.sh onnxruntime resnet50 gpu --accuracyTestScenario.SingleStream qps=130.91, mean=0.0287, time=29.983, acc=90.395%, queries=3925, tiles=50.0:0.0265,80.0:0.0285,90.0:0.0405,95.0:0.0425,99.0:0.0490,99.9:0.0512
455.00user 4.96system 1:59.43elapsed 385%CPU (0avgtext+0avgdata 975080maxresident)kqps on GPU! Pinned article: Introduction to the OurBigBook Project
Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
Intro to OurBigBook
. Source. We have two killer features:
- topics: topics group articles by different users with the same title, e.g. here is the topic for the "Fundamental Theorem of Calculus" ourbigbook.com/go/topic/fundamental-theorem-of-calculusArticles of different users are sorted by upvote within each article page. This feature is a bit like:
- a Wikipedia where each user can have their own version of each article
- a Q&A website like Stack Overflow, where multiple people can give their views on a given topic, and the best ones are sorted by upvote. Except you don't need to wait for someone to ask first, and any topic goes, no matter how narrow or broad
This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
Figure 1. Screenshot of the "Derivative" topic page. View it live at: ourbigbook.com/go/topic/derivativeVideo 2. OurBigBook Web topics demo. Source. - local editing: you can store all your personal knowledge base content locally in a plaintext markup format that can be edited locally and published either:This way you can be sure that even if OurBigBook.com were to go down one day (which we have no plans to do as it is quite cheap to host!), your content will still be perfectly readable as a static site.
- to OurBigBook.com to get awesome multi-user features like topics and likes
- as HTML files to a static website, which you can host yourself for free on many external providers like GitHub Pages, and remain in full control
Figure 3. Visual Studio Code extension installation.
Figure 4. Visual Studio Code extension tree navigation.
Figure 5. Web editor. You can also edit articles on the Web editor without installing anything locally.Video 3. Edit locally and publish demo. Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.Video 4. OurBigBook Visual Studio Code extension editing and navigation demo. Source. 
- Infinitely deep tables of contents:
All our software is open source and hosted at: github.com/ourbigbook/ourbigbook
Further documentation can be found at: docs.ourbigbook.com
Feel free to reach our to us for any help or suggestions: docs.ourbigbook.com/#contact