pycoral
Python API for ML inferencing and transfer-learning on Coral devices (by google-coral)
Keras
Deep Learning for humans (by keras-team)
Our great sponsors
pycoral | Keras | |
---|---|---|
8 | 78 | |
326 | 60,937 | |
9.5% | 0.6% | |
4.3 | 9.9 | |
2 months ago | 5 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
pycoral
Posts with mentions or reviews of pycoral.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-09-10.
-
I am trying to get Google Coral TPU (USB) to work on the latest Mac OS (Ventura). I can’t find suitable PyCoral for this OS. Only older OS are supported. I run Intel Mac Mini from 2014. My ultimate goal is to run Frigate NVR on a container on this Mac. What to do?
I’m sorry to hear that you’re having trouble finding a suitable version of PyCoral for macOS Ventura. Unfortunately, I couldn’t find any information about PyCoral support for macOS Ventura either. However, one possible solution could be to build and install PyCoral from source on your Mac using Python 3.9.x or downgrade Python to 3.8.x and install PyCoral again. You can find the PyCoral source code on GitHub. Frigate NVR is designed around the expectation that a Coral TPU is used to achieve very low inference speeds. Offloading TensorFlow to the Google Coral is an order of magnitude faster and will reduce your CPU load dramatically.
-
Best home security system/cameras
Frigate NVR is an awesome open source project that not only provides recording capabilities, but also object detection with very good performance utilizing Coral AI's EdgeTPU. It also integrates well with Home Assistant so it opens up the door to do basically whatever you want.
-
[FS] Google Coral Mini PCIe Accelerator w/ PCIe adapter
I’ve never heard of Google Coral but it’s really neat. https://coral.ai
-
Zwave with docker on synology not working
I guess because it'll be supported longer? I don't use photos or moments, so there were zero reasons to upgrade. I would lose out of the box ability to use my zwave/zigbee stick and coral.ai stick without workarounds. Plex migration to DSM7 seemed like a pain also (with more drawbacks), but I moved my Plex install to Docker to prevent any future breaking from DSM updates
-
IP Camera monitor with Atomic Pi and Coral Edge TPU
I checked all the official distributors listed on https://coral.ai/products/accelerator/ and Mouser has ~1300 in stock (https://www.mouser.com/ProductDetail/Coral/G950-01456-01) for MSRP of $59.99. There are some on Amazon marked up to $85.
-
Can Google Coral be installed in a DS916+?
Google Coral is an offline/private image processor and is pretty amazing. You can read about it here: https://coral.ai/
-
Machine Learning HomeLab
Have you considered a Google Coral?
-
How do you install TF (and TFLite) on a Raspberry Pi Zero?
'pip3 install https://github.com/google-coral/pycoral/releases/download/release-frogfish/tflite_runtime-2.5.0-cp37-cp37m-linux_armv7l.whl'
Keras
Posts with mentions or reviews of Keras.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-27.
-
Library for Machine learning and quantum computing
Keras
-
My Favorite DevTools to Build AI/ML Applications!
As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development.
- Release: Keras 3.3.0
-
Getting Started with Gemma Models
After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow.
-
Keras 3.0
All breaking changes are listed here: https://github.com/keras-team/keras/issues/18467
You can use this migration guide to identify and fix each of these issues (and further, making your code run on JAX or PyTorch): https://keras.io/guides/migrating_to_keras_3/
- Keras 3: A new multi-back end Keras
-
Can someone explain how keras code gets into the Tensorflow package?
I'm guessing the "real" keras code is coming from the keras repository. Is that a correct assumption? How does that version of Keras get there? If I wanted to write my own activation layer next to ELU, where exactly would I do that?
-
How popular are libraries in each technology
Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks.
-
List of AI-Models
Click to Learn more...
-
Official Question Thread! Ask /r/photography anything you want to know about photography or cameras! Don't be shy! Newbies welcome!
I'm not aware of anything off-the-shelf, but if you have sufficient programming experience, one way to do this would be to build a large dataset of reference images and pictures and use something like keras to train a convolutional neural network on them.