Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality. Learn more →
Top 23 Coreml Open-Source Projects
-
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
catboost
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
-
-
MMdnn
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
-
SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
-
TNN
TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and performance optimization for mobile devices, and also draws on the advantages of good extensibility and high performance from existed open source efforts
-
coremltools
Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
-
PINTO_model_zoo
A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
-
CoreML-in-ARKit
Simple project to detect objects and display 3D labels above them in AR. This serves as a basic Template for an ARKit project to use CoreML.
-
AnimeGANv3
Use AnimeGANv3 to make your own animation works, including turning photos or videos into anime.
-
-
Apple-Silicon-Guide
Apple Silicon Guide. Learn all about the A17 Pro, A16 Bionic, R1, M1-series, M2-series, and M3-series chips. Along with all the Devices, Operating Systems, Tools, Gaming, and Software that Apple Silicon powers.
-
Lumina
A camera designed in Swift for easily integrating CoreML models - as well as image streaming, QR/Barcode detection, and many other features
-
EdgeSAM
Official PyTorch implementation of "EdgeSAM: Prompt-In-the-Loop Distillation for On-Device Deployment of SAM"
-
-
tflite2tensorflow
Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite, ONNX, OpenVINO, Myriad Inference Engine blob and .pb from .tflite. Support for building environments with Docker. It is possible to directly access the host PC GUI and the camera to verify the operation. NVIDIA GPU (dGPU) support. Intel iHD GPU (iGPU) support. Supports inverse quantization of INT8 quantization model.
-
SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Ref https://www.youtube.com/watch?v=0GwnxFNfZhM https://github.com/ultralytics/yolov5 https://dev.to/gfstealer666/kaaraich-yolo-alkrithuemainkaartrwcchcchabwatthu-object-detection-3lef https://www.kaggle.com/datasets/devdgohil/the-oxfordiiit-pet-dataset/data
Project mention: What's the best PyTorch model visualization tool? | news.ycombinator.com | 2024-04-28Netron seems to be the best that I've seen so far. https://github.com/lutzroeder/netron
Project mention: CatBoost: Open-source gradient boosting library | news.ycombinator.com | 2024-03-05
Project mention: How do I install stable diffusion on my Mac Air? My midjourney subscription expired, and I don’t wanna pay another 60 a month. So I wanna try a new service. | /r/StableDiffusion | 2023-07-12Mochi Diffusion is very fast but you're limited to 512x512 or whatever image sizes the model is generated on
Project mention: CoreML commit from Apple mentions iOS17 exclusive features | /r/u_Standard-Sundae-6011 | 2023-06-03
yep. they have a neural engine that is separate from the CPU and GPU that does really fast matmuls https://github.com/hollance/neural-engine. it's basically completely undocumented.
Congratulations for buying a NON-PC that's IN-capable of playing games on its own except these games https://www.pcgamingwiki.com/wiki/List_of_macOS_ARM_games. You paid top dollars for a crippled machine, all thanks to Apple. Any ARM user "Must" use one of the PC Emulators told in https://github.com/mikeroyal/Apple-Silicon-Guide which is usually Rosetta (default by Apple) for you so that you can play ANY game you see but NOT in its native speed but in Emulated (slower but not much) speed.
Project mention: EdgeSAM: Prompt-in-the-Loop Distillation for On-Device Deployment of Sam | news.ycombinator.com | 2023-12-12Not affiliated with the authors but find the topic interesting. They have a GitHub page with code and a short demo also:
https://github.com/chongzhou96/EdgeSAM
Project mention: I made an app that runs Mistral 7B 0.2 LLM locally on iPhone Pros | news.ycombinator.com | 2024-01-07Conceptually, to the best of my understanding, nothing too serious; perhaps the inefficiency of processing a larger input than necessary?
Practically, a few things:
If you want to have your cake & eat it too, they recommend Enumerated Shapes[1] in their coremltools docs, where CoreML precompiles up to 128 (!) variants of input shapes, but again this is fairly limiting (1 tok, 2 tok, 3 tok... up to 128 token prompts.. maybe you enforce a minimum, say 80 tokens to account for a system prompt, so up to 200 tokens, but... still pretty short). But this is only compatible with CPU inference, so that reduces its appeal.
It seems like its current state was designed for text embedding models, where you normalize input length by chunking (often 128 or 256 tokens) and operate on the chunks — and indeed, that’s the only text-based CoreML model that Apple ships today, a Bert embedding model tuned for Q&A[2], not an LLM.
You could used a fixed input length that’s fairly large; I haven’t experimented with it once I grasped the memory requirements, but from what I gather from HuggingFace’s announcement blog post[3], it seems that is what they do with swift-transformers & their CoreML conversions, handling the details for you[4][5]. I haven’t carefully investigated the implementation, but I’m curious to learn more!
You can be sure that no one is more aware of all this than Apple — they published "Deploying Transformers on the Apple Neural Engine" in June 2022[6]. I look forward to seeing what they cook up for developers at WWDC this year!
---
[1] "Use `EnumeratedShapes` for best performance. During compilation the model can be optimized on the device for the finite set of input shapes. You can provide up to 128 different shapes." https://apple.github.io/coremltools/docs-guides/source/flexi...
[2] BertSQUAD.mlmodel (fp16) https://developer.apple.com/machine-learning/models/#text
[3] https://huggingface.co/blog/swift-coreml-llm#optimization
[4] `use_fixed_shapes` "Retrieve the max sequence length from the model configuration, or use a hardcoded value (currently 128). This can be subclassed to support custom lengths." https://github.com/huggingface/exporters/pull/37/files#diff-...
[5] `use_flexible_shapes` "When True, inputs are allowed to use sequence lengths of `1` up to `maxSequenceLength`. Unfortunately, this currently prevents the model from running on GPU or the Neural Engine. We default to `False`, but this can be overridden in custom configurations." https://github.com/huggingface/exporters/pull/37/files#diff-...
[6] https://machinelearning.apple.com/research/neural-engine-tra...
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Coreml related posts
-
I made an app that runs Mistral 7B 0.2 LLM locally on iPhone Pros
-
M2 Ultra can run 128 streams of Llama 2 7B in parallel
-
Apple is adding more and more neural engine cores to their products, is there any way to use them for local LLMs?
-
CoreML commit from Apple mentions iOS17 exclusive features
-
CoreML commit from Apple mentions iOS17 exclusive features
-
CoreML commit from Apple mentions iOS17 exclusive features
-
Lisa Su Saved AMD. Now She Wants Nvidia's AI Crown
-
A note from our sponsor - InfluxDB
www.influxdata.com | 1 May 2024
Index
What are some of the best open-source Coreml projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | yolov5 | 46,921 |
| 2 | netron | 26,110 |
| 3 | catboost | 7,744 |
| 4 | MochiDiffusion | 7,107 |
| 5 | CoreML-Models | 6,221 |
| 6 | MMdnn | 5,780 |
| 7 | macdriver | 4,345 |
| 8 | TNN | 4,281 |
| 9 | coremltools | 4,063 |
| 10 | iOS 11 by Examples | 3,320 |
| 11 | PINTO_model_zoo | 3,301 |
| 12 | neural-engine | 1,861 |
| 13 | CoreML-in-ARKit | 1,650 |
| 14 | AnimeGANv3 | 1,573 |
| 15 | NSFWDetector | 1,554 |
| 16 | Apple-Silicon-Guide | 1,552 |
| 17 | CoreML-Models | 1,132 |
| 18 | Lumina | 892 |
| 19 | iowncode | 860 |
| 20 | EdgeSAM | 685 |
| 21 | exporters | 526 |
| 22 | tflite2tensorflow | 249 |
| 23 | BackgroundRemoval | 194 |
Sponsored
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com