Судя по прошлому опросу просили полнотекстовую статью.
В прошлый раз по итогу конкурса сил хватило только на пост на канале. В этот раз я разродился чутка причесать код, выложить тетрадки и написать целый длинный блог пост. По сути было весело:
- новый домен - видео - и сгенерирована тонна копипасты для работы с ним в тетрадках;
- новые sota модели для изучения;
- изучен и весьма распробован новый фреймворк - pytorch;
Статья
- https://spark-in.me/post/fish-object-detection-ssd-yolo
Комментируйте, репостите, шлите друзьям, критикуйте.
И как всегда можно:
- Поставить оценку каналу тут - https://telegram.me/tchannelsbot?start=snakers4 (1000+ подписчиков и только 50+ оценок - 5% как бы норм, но почему не больше?)
- Задонатить на новые статьи и развитие канала (вести канал несложно, статьи и соревнования занимают очень много времени) тут:
-- На чай - https://goo.gl/zveIOr
-- Договор ТКС 5011673505
#data_science
#deep_learning
#computer_vision
В прошлый раз по итогу конкурса сил хватило только на пост на канале. В этот раз я разродился чутка причесать код, выложить тетрадки и написать целый длинный блог пост. По сути было весело:
- новый домен - видео - и сгенерирована тонна копипасты для работы с ним в тетрадках;
- новые sota модели для изучения;
- изучен и весьма распробован новый фреймворк - pytorch;
Статья
- https://spark-in.me/post/fish-object-detection-ssd-yolo
Комментируйте, репостите, шлите друзьям, критикуйте.
И как всегда можно:
- Поставить оценку каналу тут - https://telegram.me/tchannelsbot?start=snakers4 (1000+ подписчиков и только 50+ оценок - 5% как бы норм, но почему не больше?)
- Задонатить на новые статьи и развитие канала (вести канал несложно, статьи и соревнования занимают очень много времени) тут:
-- На чай - https://goo.gl/zveIOr
-- Договор ТКС 5011673505
#data_science
#deep_learning
#computer_vision
Spark in me
Identify fish challenge - playing with object detection
My path to learning SSD and YOLO and my experience in participating in a video object search competition with 300+GB of data
Статьи автора - http://spark-in.me/author/snakers41
Блог - http://spark-in.me
Статьи автора - http://spark-in.me/author/snakers41
Блог - http://spark-in.me
Pillow-SIMD is a Pillow fork, that claims 3-6x faster performance on CPU using same resources
- https://github.com/uploadcare/pillow-simd
- https://habrahabr.ru/post/301576/
It claims to be this easy
#computer_vision
- https://github.com/uploadcare/pillow-simd
- https://habrahabr.ru/post/301576/
It claims to be this easy
$ pip uninstall pillow
$ CC="cc -mavx2" pip install -U --force-reinstall pillow-simd
#computer_vision
GitHub
GitHub - uploadcare/pillow-simd: The friendly PIL fork
The friendly PIL fork. Contribute to uploadcare/pillow-simd development by creating an account on GitHub.
When I started doing CV - this page was quite scarce.
Now it's full and amazing!
I recommend this page as your go-to reference for already implemented non CNN based (classic) CV. It is just amazing. Simple and illustrative examples with code.
This totally eliminates the need in open-cv abomination =)
http://scikit-image.org/docs/dev/auto_examples/index.html
Best libraries for images I have seen so far
- pillow (pillow simd)
- skimage
- imageio
- scikit video
- moviepy
#data_science
#computer_vision
Now it's full and amazing!
I recommend this page as your go-to reference for already implemented non CNN based (classic) CV. It is just amazing. Simple and illustrative examples with code.
This totally eliminates the need in open-cv abomination =)
http://scikit-image.org/docs/dev/auto_examples/index.html
Best libraries for images I have seen so far
- pillow (pillow simd)
- skimage
- imageio
- scikit video
- moviepy
#data_science
#computer_vision
Yolov3 - best paper.
But not in terms of scientific contribution, but rebuttal of DS community BS.
Very funny read.
- https://pjreddie.com/media/files/papers/YOLOv3.pdf
If you want a proper comparison of object detection algorithms - use this paper https://arxiv.org/abs/1611.10012
Looks like SSD and YOLO are reasonably good and fast, and RCNN can be properly tuned to be 3-5x slower (not 100x) and more accurate.
#data_science
#computer_vision
But not in terms of scientific contribution, but rebuttal of DS community BS.
Very funny read.
- https://pjreddie.com/media/files/papers/YOLOv3.pdf
If you want a proper comparison of object detection algorithms - use this paper https://arxiv.org/abs/1611.10012
Looks like SSD and YOLO are reasonably good and fast, and RCNN can be properly tuned to be 3-5x slower (not 100x) and more accurate.
#data_science
#computer_vision
Amazing articles about image hashing
Also a python library
- Library https://github.com/JohannesBuchner/imagehash
- Articles:
https://fullstackml.com/wavelet-image-hash-in-python-3504fdd282b5http://www.hackerfactor.com/blog/index.php?/archives/529-Kind-of-Like-That.html
http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
http://www.hackerfactor.com/blog/index.php?/archives/529-Kind-of-Like-That.html
#data_science
#computer_vision
Also a python library
- Library https://github.com/JohannesBuchner/imagehash
- Articles:
https://fullstackml.com/wavelet-image-hash-in-python-3504fdd282b5http://www.hackerfactor.com/blog/index.php?/archives/529-Kind-of-Like-That.html
http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
http://www.hackerfactor.com/blog/index.php?/archives/529-Kind-of-Like-That.html
#data_science
#computer_vision
GitHub
GitHub - JohannesBuchner/imagehash: A Python Perceptual Image Hashing Module
A Python Perceptual Image Hashing Module. Contribute to JohannesBuchner/imagehash development by creating an account on GitHub.
How to solve an arbitrary CV task ...
- W/o annotation
- W/o GPUs in production
- And make your model work in real life and help people
https://spark-in.me/post/deploy_classifier
https://medium.com/@slizhikova.a.v/how-to-get-your-own-image-classifier-region-labelling-model-without-annotation-d95aabbd8599?sk=fd844bf2a6f48171f02cbbda7bc493a6
#deep_learning
#computer_vision
- W/o annotation
- W/o GPUs in production
- And make your model work in real life and help people
https://spark-in.me/post/deploy_classifier
https://medium.com/@slizhikova.a.v/how-to-get-your-own-image-classifier-region-labelling-model-without-annotation-d95aabbd8599?sk=fd844bf2a6f48171f02cbbda7bc493a6
#deep_learning
#computer_vision