CenterNet
NumPy
CenterNet | NumPy | |
---|---|---|
6 | 272 | |
7,101 | 26,413 | |
- | 1.1% | |
0.0 | 10.0 | |
about 1 year ago | 2 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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CenterNet
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Seeking AI Model to Predict the Center of an Object in Images
CenterNet comes to mind
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[D] Cross Shape Artifact in Heatmap
Found relevant code at https://github.com/xingyizhou/CenterNet + all code implementations here
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CUDA errors while trying to run CenterNet
I am trying the implement this paper https://github.com/xingyizhou/CenterNet
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I want to create a pill counter using points instead of bounding boxes. What model should I train from?
Take a look at this centernet architecture.
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Using PyTorch and NumPy? You're making a mistake
Yeah, I'd run into this 2 years ago and ended up also reporting an issue on the Centernet repo [1]
The solution I have in that repo adapts from the very helpful discussions in the original Pytorch issue [2]
I will admit that this is *very* easy to mess up as evidenced by the fact that examples in the official tutorials for Pytorch and other well known code-bases suffer from it. In the Pytorch training framework I've developed at work, we've implemented a custom `worker_init_fn` as outlined in [1] that is the default for all "trainer" instances who are responsible for instantiating DataLoaders in 99% of our training runs.
[1] https://github.com/xingyizhou/CenterNet/issues/233
[2] https://github.com/pytorch/pytorch/issues/5059
- [P] Using PyTorch + NumPy? A bug that plagues thousands of open-source ML projects.
NumPy
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
- NumPy 2.0.0 Beta1
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Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.
- JSON dans les projets data science : Trucs & Astuces
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JSON in data science projects: tips & tricks
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:
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Introducing Flama for Robust Machine Learning APIs
numpy: A library for scientific computing in Python
- help with installing numpy, please
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A Comprehensive Guide to NumPy Arrays
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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NumPy 2.0 development status & announcements: major C-API and Python API cleanup
I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]
What are some alternatives?
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
SymPy - A computer algebra system written in pure Python
mmdetection - OpenMMLab Detection Toolbox and Benchmark
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
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
blaze - NumPy and Pandas interface to Big Data
Kornia - Geometric Computer Vision Library for Spatial AI
SciPy - SciPy library main repository
pose-tensorflow - Human Pose estimation with TensorFlow framework
Numba - NumPy aware dynamic Python compiler using LLVM
tensorflow - An Open Source Machine Learning Framework for Everyone
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).