CoreML-Models
Caffe2
| CoreML-Models | Caffe2 | |
|---|---|---|
| 2 | - | |
| 6,231 | 8,443 | |
| - | - | |
| 2.3 | 0.0 | |
| 11 months ago | over 5 years ago | |
| Python | Jupyter Notebook | |
| MIT License | - |
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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.
CoreML-Models
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I made an app completely on SwiftUI dedicated to browsing vehicles for sale on eBay. It got rejected for being too basic, should I justify any more time on this?
Super far fetched idea on passing 4.2 with iOS specific functionality: there are CoreML models specifically capable of identifying car makes and models (linked in this repo), which could allow you to take/select a photo, and automatically identify/search the car based on the prediction. That being said, it will not take into account nearly as many details as you can manually specify in your app. Nice work!
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[MacOs/Node JS] Is there a simple hand tracking library that works in node.js?
Mac? Use the built in coreml; https://github.com/likedan/Awesome-CoreML-Models
Caffe2
We haven't tracked posts mentioning Caffe2 yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
Swift-AI - The Swift machine learning library.
Caffe - Caffe: a fast open framework for deep learning.
Tensorflow-iOS
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
CoreML-samples - Sample code for Core ML using ResNet50 provided by Apple and a custom model generated by coremltools.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
BrainCore - The iOS and OS X neural network framework
Serpent.AI - Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!
MLKit - A simple machine learning framework written in Swift 🤖
Porcupine  - On-device wake word detection powered by deep learning
Theano - Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is being continued as PyTensor: www.github.com/pymc-devs/pytensor