frugally-deep
albumentations
| frugally-deep | albumentations | |
|---|---|---|
| 5 | 28 | |
| 1,045 | 13,451 | |
| - | 1.1% | |
| 8.0 | 8.9 | |
| 13 days ago | 1 day ago | |
| C++ | 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.
frugally-deep
- Frugally-deep: Header-only library for using Keras (TensorFlow) models in C++
-
Ask HN: What side projects landed you a job?
The interview for my current job first went mediocre, but by talking about frugally-deep (a side project of mine) I was able to excite my (now) employer. :-)
https://github.com/Dobiasd/frugally-deep
-
Way to convert keras based model to C++.
Did you work through the FAQ here: https://github.com/Dobiasd/frugally-deep/blob/master/FAQ.md
- Handwritten digit recognition using CircuitPython, Raspberry Pi Pico, OV7670 and 120x160 TFT LCD. r/circuitpython - Handwritten digit recognition using CircuitPython, Raspberry Pi Pico, OV7670 and 120x160 TFT LCD.
albumentations
-
Augment specific classes?
You can use albumentations if you are comfortable with using open source libraries https://github.com/albumentations-team/albumentations
-
Ask HN: What side projects landed you a job?
One of the members of the core team of our open-source library https://albumentations.ai/
It was not the only reason he was hired; it was a solid addition to his already good performance at the interviews.
Or at least that is what the hiring manager later said.
-
The Lack of Compensation in Open Source Software Is Unsustainable
I am one of the creators and maintainers of https://albumentations.ai/.
- 12800+ stars
-
Burn Deep Learning Framework Release 0.7.0: Revamped (de)serialization, optimizer & module overhaul, initial ONNX support and tons of new features.
Is something planned to support data augmentations? Something like https://albumentations.ai/
-
How to label augmented images for training YOLO algorithm?
Here you go: https://albumentations.ai/
-
Unstable Diffusion bounces back with $19,000 raised in one day, by using Stripe
I think they should use some data augmentation techniques like I am using for Infinity AI if you wanna see more here. Note that most of these do not work for image generation.
-
Tokyo Drift : detecting drift in images with NannyML and Whylogs
Our second approach was a more automated one. Here the idea was to try out an image augmentation library, Albumentations, and use it for adversarial attacks. This time, instead of one-shot images, we applied the transformations at random time ranges. We chose for these transformations also to be more subtle than then one-shot images, such as vertical flips, grayscaling, downscaling, …
-
[D] Improve machine learning with same number of images
Check out albumentations. If your use case is segmentation, check out the offline augmentation of this project
-
What are the best programs/scripts for image augmentation of YOLO5 training dataset. Something like roboflow but free)
I think this is the most popular open source project: https://github.com/albumentations-team/albumentations
-
To get dataset for face image restoration.
You can also curate your own dataset by using open source images (https://universe.roboflow.com/search?q=faces%20images%3E1000) and open source augmentations (https://github.com/albumentations-team/albumentations). Or you can do use the augmentation UI (https://docs.roboflow.com/image-transformations/image-augmentation) to apply noise, blurring, shear, crop, etc.
What are some alternatives?
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
imgaug - Image augmentation for machine learning experiments.
tensorflow - An Open Source Machine Learning Framework for Everyone
YOLO-Mosaic - Perform mosaic image augmentation on data for training a YOLO model
tiny-cnn - header only, dependency-free deep learning framework in C++14
labelme2coco - A lightweight package for converting your labelme annotations into COCO object detection format.
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System
autoalbument - AutoML for image augmentation. AutoAlbument uses the Faster AutoAugment algorithm to find optimal augmentation policies. Documentation - https://albumentations.ai/docs/autoalbument/
Genann - simple neural network library in ANSI C
Mask-RCNN-TF2 - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0
nano
BlenderProc - A procedural Blender pipeline for photorealistic training image generation