Alternatives

Products that do what Faster R-CNN object detector implemented in PyTorch and TensorFlow 2 does

Fresh and (I think) clean implementations of Faster R-CNN in PyTorch and TensorFlow 2/Keras. I wanted to learn about object detectors and decided to understand and implement a foundational model in the field, Faster R-CNN (elements of which are still used in modern models to this day), using the paper alone. That proved to be more difficult than expected and I had to relent and take a peak at existing implementations to fill in some important gaps. I've documented my struggles and learnings in the README for others to benefit from. I also wanted to solidify my understanding of…

1
TensorFlow 1.0▲820
Open-source machine learning library by Google
2017 · tensorflow.org
2
Weights & Biases▲110
Developer tools for deep learning & machine learning
2019
3CT
Comprehensive Tutorials in Deep Learning Using TensorFlow▲327
2018 · github.com
4
Annot8▲140
The fastest way to tag images for object detection datasets
2025
5IS
I stripped DALL·E Mini to its bare essentials and converted it to Torch▲663
2022 · github.com
6SA
Simple and comprehensive tutorials in Deep Learning using TensorFlow▲157
2018 · github.com
7BM
Boltzmann Machines in TensorFlow with Examples: RBM, DBM, AIS▲201
2017 · github.com
8DL
Deep Learning in TensorFlow – The Roadmap for Study and Learning▲146
2019 · github.com
9FD
Fast Deep Reinforcement Learning Course▲148
I worked on this applied Deep Reinforcement Learning course for the better part of 2021. I made a Datacamp course [0] before, and this served as my inspiration to make an applied Deep RL series. Normally, Deep RL courses teach a lot of mathematically involved theory. You get the practical applications near the end (if at all). I have tried to turn that on its head. In the top-down approach, you learn practical skills first, then go deeper later. This is much more fun. This course (the first in a planned multi-part series) shows how to use the Deep Reinforcement Learning framework RLlib to…
2022 · courses.dibya.online
10
Tensorflow Research Cloud▲288
Accelerating open machine learning research with Cloud TPUs
2017
11
TensorFlow 1.0▲80
Open-source machine learning library by Google
2018
12TR
TensorFlow-Resources – Organized and Useful Resources about TensorFlow▲152
2018 · github.com
13RT
Run TRELLIS.2 Image-to-3D generation natively on Apple Silicon▲202
I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels: none of which work on Mac. I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files. Generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro (24GB). Not as fast as…
Apr 2026 · github.com
14RA
Richard – A CNN written in C++ and Vulkan (no ML or math libs)▲184
This started out as a personal effort to learn more about machine learning. It's currently a CLI app where you give it a JSON file specifying your network architecture and hyperparameters and point it to your training data, then invoke it again in 'eval' mode with some data it's not seen before and it will try to classify each sample. I don't see many other people using Vulkan for GPGPU, and there may be many good reasons for that, but I wanted to try something a bit different. I've made every attempt to make the code very clean and readable and I've written up the math in…
2024 · github.com
15
Nyckel Object Detection▲84
The machine learning API for non-experts
2022
16
R-CNN Trainer▲1
R-CNN Trainer is a application for object detection models.
Jul 2026 · apps.microsoft.com
17AG
A general-purpose encoder-decoder framework for Tensorflow▲54
2017 · github.com
18DL
Deep Learning Using TensorFlow – Tutorials and Source Code▲31
2017 · github.com
19IB
I built a tensor library from scratch in C++/CUDA▲119
Hi HN, Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally. The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++. Next on the roadmap is…
2025 · github.com
20ET
End-to-end deep learning experimentation platform for Tensorflow▲151
2017 · github.com
21
Machine Learning Playground▲169
Breathtaking visuals for learning ML techniques.
2017
22LF
LeanRL: Fast PyTorch RL with Torch.compile and CUDA Graphs▲53
We're excited to announce that we've open-sourced LeanRL, a lightweight PyTorch reinforcement learning library that provides recipes for fast RL training using torch.compile and CUDA graphs. By leveraging these tools, we've achieved significant speed-ups compared to the original CleanRL implementations - up to 6x faster! Reinforcement learning is notoriously CPU-bound due to the high frequency of small CPU operations. PyTorch's powerful compiler can help alleviate these issues, but comes with its own costs. LeanRL addresses this challenge by providing simple recipes to accelerate your…
2024 · github.com
23
TensorFire▲99
Blazing-fast in-browser neural networks
2017
24
Forge Agent▲113
Swarm Agents That Turn Slow PyTorch Into Fast GPU Kernels
Jan 2026

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