Towards-Explainable-AI-System-for-Traffic-Sign-Recognition-and-Deployment-in-a-Simulated-Environment
caer
| Towards-Explainable-AI-System-for-Traffic-Sign-Recognition-and-Deployment-in-a-Simulated-Environment | caer | |
|---|---|---|
| 2 | 8 | |
| 45 | 749 | |
| - | - | |
| 0.0 | 0.0 | |
| almost 3 years ago | 7 months ago | |
| C# | Python | |
| MIT License | MIT License |
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Towards-Explainable-AI-System-for-Traffic-Sign-Recognition-and-Deployment-in-a-Simulated-Environment
- Simulation based Traffic Sign Recognition Benchmark - A simulation framework developed for training autonomous-driving systems for traffic sign recognition
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Simulation-based Traffic Sign Recognition Benchmark (STSRB)
Check out the GitHub link for more information: https://github.com/alen-smajic/Towards-Explainable-AI-System-for-Traffic-Sign-Recognition-and-Deployment-in-a-Simulated-Environment
caer
- Show HN: Caer – A lightweight GPU-accelerated Vision library in Python
- I wrote a lightweight GPU-accelerated Vision library in Python
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Jetson nano python3 illegal instruction problem
I think it may have. If you look at line 10 of https://github.com/jasmcaus/caer/blob/master/configs.ini, you’ll see that caer has numpy and opencv-contrib-python dependencies that get referenced in its setup.py. If I recall correctly, pip on the nano doesn’t pick up the default numpy and opencv-python system installs, so when you go to install something like caer that has them as dependencies, it will install new copies except the wheel files that it grabs are incompatible. The solution I have found to work is to run something similar to the command above: “pip3 install —no-binary caer —no-binary numpy—no-binary opencv-contrib-python —no-binary typing-extensions —no-binary mypy —force-reinstall caer”. Some of those —no-binary options may not be necessary but they’ll at least ensure pip grabs the source for each of the dependencies and rebuilds it locally rather than using an imcompatible version. This command will take awhile! But you only should have to do it once.
- jasmcaus/caer Modern Computer Vision on the Fly
- Caer: High-performance Vision Library in Python (faster than Torchvision)
- Caer – A GPU-accelerated Computer Vision library (faster than Torchvision)
- jasmcaus/caer lightweight, scalable Computer Vision library for high-performance AI research
- Caer – A GPU-Accelerated Computer Vision Library in Python
What are some alternatives?
flowframes - Flowframes Windows GUI for video interpolation using DAIN (NCNN) or RIFE (CUDA/NCNN)
fiftyone - The open-source tool for building high-quality datasets and computer vision models
OpenSeeFace - Robust realtime face and facial landmark tracking on CPU with Unity integration
img2table - img2table is a table identification and extraction Python Library for PDF and images, based on OpenCV image processing
Alturos.Yolo - C# Yolo Darknet Wrapper (real-time object detection)
opencv - Haskell binding to OpenCV-3.x
com.unity.perception - Perception toolkit for sim2real training and validation in Unity
Single-Image-Dehazing-Python - python implementation of the paper: "Efficient Image Dehazing with Boundary Constraint and Contextual Regularization"
ailab - Experience, Learn and Code the latest breakthrough innovations with Microsoft AI
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
moviepy - Video editing with Python