Pytorch
mediapipe
Pytorch | mediapipe | |
---|---|---|
349 | 49 | |
79,328 | 25,946 | |
1.7% | 1.5% | |
10.0 | 9.9 | |
5 days ago | 1 day ago | |
Python | C++ | |
BSD 1-Clause License | Apache License 2.0 |
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.
Pytorch
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Mathematics secret behind AI on Digit Recognition
Hi everyone! I’m devloker, and today I’m excited to share a project I’ve been working on: a digit recognition system implemented using pure math functions in Python. This project aims to help beginners grasp the mathematics behind AI and digit recognition without relying on high-level libraries like TensorFlow or PyTorch. You can find the complete code on my GitHub repository.
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Top 17 Fast-Growing Github Repo of 2024
PyTorch
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AMD's MI300X Outperforms Nvidia's H100 for LLM Inference
> their own custom stack to interact with GPUs
lol completely made up.
are you conflating CUDA the platform with the C/C++ like language that people write into files that end with .cu? because while some people are indeed not writing .cu files, absolutely no one is skipping the rest of the "stack".
source: i work at one of these "mega corps". hell if you don't believe me go look at how many CUDA kernels pytorch has https://github.com/pytorch/pytorch/tree/main/aten/src/ATen/n....
> Everybody thinks it’s CUDA that makes Nvidia the dominant player.
it 100% does
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Awesome List
PyTorch - An open source machine learning framework. PyTorch Tutorials - Tutorials and documentation.
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Understanding GPT: How To Implement a Simple GPT Model with PyTorch
In this guide, we provided a comprehensive, step-by-step explanation of how to implement a simple GPT (Generative Pre-trained Transformer) model using PyTorch. We walked through the process of creating a custom dataset, building the GPT model, training it, and generating text. This hands-on implementation demonstrates the fundamental concepts behind the GPT architecture and serves as a foundation for more complex applications. By following this guide, you now have a basic understanding of how to create, train, and utilize a simple GPT model. This knowledge equips you to experiment with different configurations, larger datasets, and additional techniques to enhance the model's performance and capabilities. The principles and techniques covered here will help you apply transformer models to various NLP tasks, unlocking the potential of deep learning in natural language understanding and generation. The methodologies presented align with the advancements in transformer models introduced by Vaswani et al. (2017), emphasizing the power of self-attention mechanisms in processing sequences of data more effectively than traditional approaches (Vaswani et al., 2017). This understanding opens pathways to explore and innovate in the field of natural language processing using cutting-edge deep learning techniques (Kingma & Ba, 2015).
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Building a Simple Chatbot using GPT model - part 2
PyTorch is a powerful and flexible deep learning framework that offers a rich set of features for building and training neural networks.
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Clusters Are Cattle Until You Deploy Ingress
Oddly enough, sometimes, the best way to learn is by putting forth incorrect opinions or questions. Recently, while wrestling with AI project complexities, I pondered aloud whether all Docker images with AI models would inevitably be bulky due to PyTorch dependencies. To my surprise, this sparked many helpful responses, offering insights into optimizing image sizes. Being willing to be wrong opens up avenues for rapid learning.
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Tinygrad 0.9.0
Tinygrad targets consumer hardware (to be precise, only Radeon 7900XTX and nothing else[1]), while ROCm does not actually provide good support for such hardware. For example, last release of hipBLASLt-6.1.1 library has deep integration with PyTorch[1], while working only on AMD Instinct hardware. And even for the professional hardware out there, the support period is ridiculous: AMD Instinct MI100 (2020) is not supported. Only 4 years and tens of thousands of dollars worth of hardware is going to the trash, yay!
And to be more precise, they still use some core libraries from ROCm stack[3], they just don't use all these fancy multi-gigabyte[4] hardware-limited rocBLAS/hipBLASlt/rocWMMA/rocRAND/etc. libraries.
[1] https://tinygrad.org/#tinybox
[2] https://github.com/pytorch/pytorch/issues/119081
[3] https://github.com/tinygrad/tinygrad/blob/v0.9.0/tinygrad/ru...
[4] https://repo.radeon.com/rocm/yum/6.1.1/main/
- PyTorch 2.3: User-Defined Triton Kernels, Tensor Parallelism in Distributed
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Clasificador de imágenes con una red neuronal convolucional (CNN)
PyTorch (https://pytorch.org/)
mediapipe
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Mediapipe openpose Controlnet model for SD
mediapipe/docs/solutions/pose.md at master · google/mediapipe · GitHub
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MEDIAPIPE on-device diffusion plugins for conditioned text-to-image generation
Today, we announce MediaPipe diffusion plugins, which enable controllable text-to-image generation to be run on-device. Expanding upon our prior work on GPU inference for on-device large generative models, we introduce new low-cost solutions for controllable text-to-image generation that can be plugged into existing diffusion models and their Low-Rank Adaptation (LoRA) variants.
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Running a TensorFlow object detector model and drawing boxes around objects at 60 FPS - all in React Native / JavaScript!
You can just grab the TFLite version! https://github.com/google/mediapipe/blob/master/docs/solutions/models.md
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OpenAI came after our domain because we use GPT in it
I believe Google already released transformers under an apache 2 license with a patent grant:
https://github.com/google/mediapipe/blob/master/mediapipe/mo...
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Open source Background Remover: Remove Background from images and video using AI
I was going to say that I like the MediaPipe Selfie Segmentation model for doing this sort of thing in a web page, but I've just noticed (when getting the GitHub link[1]) that Google have marked the code as legacy[2] ... no idea if the new solution is better/easier to use[3].
For what it's worth, my CodePen using the old model is here: https://codepen.io/kaliedarik/pen/PopBxBM
[1] - https://github.com/google/mediapipe/blob/master/docs/solutio...
[2] - "Attention: Thank you for your interest in MediaPipe Solutions. As of April 4, 2023, this solution was upgraded to a new MediaPipe Solution."
[3] - https://developers.google.com/mediapipe/solutions/vision/ima...
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[P] Pattern recognition
I have used mediapipe very successfully in multiple projects and it's very easy to get running. You can choose from many different vision tasks including hand landmarks ( https://github.com/google/mediapipe/blob/master/docs/solutions/hands.md )
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Getting face feature pose statistics
I found MediaPipe's Face Mesh and was impressed with how simple it was to get going, but it just gives you the landmark points and I've not gone any further yet.
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New ControlNet Face Model
We've trained ControlNet on a subset of the LAION-Face dataset using modified output from MediaPipe's face mesh annotator to provide a new level of control when generating images of faces.
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Trained an ML model using TensorFlow.js to classify American Sign Language (ASL) alphabets on browser. We are creating an open-source platform and would love to receive your feedback on our project.
Medipaipe library link: https://mediapipe.dev/
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mediapipe VS daisykit - a user suggested alternative
2 projects | 24 Mar 2023
What are some alternatives?
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
ue4-mediapipe-plugin - UE4 MediaPipe plugin
flax - Flax is a neural network library for JAX that is designed for flexibility.
AlphaPose - Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
BlazePose-tensorflow - A third-party Tensorflow Implementation for paper "BlazePose: On-device Real-time Body Pose tracking".
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
jeelizFaceFilter - Javascript/WebGL lightweight face tracking library designed for augmented reality webcam filters. Features : multiple faces detection, rotation, mouth opening. Various integration examples are provided (Three.js, Babylon.js, FaceSwap, Canvas2D, CSS3D...).
Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java
pifuhd - High-Resolution 3D Human Digitization from A Single Image.