A PyTorch implementation of YOLOv5

A PyTorch implementation of YOLOv5.

This repository has two features:

• It is pure python code and can be run immediately using PyTorch 1.4 without build
• Simplified construction and easy to understand how the model works

The model is based on ultralytics’ repo,

and the code is using the structure of TorchVision.

Requirements

• Windows or Linux, with Python ≥ 3.6
• PyTorch** ≥ 1.4.0**
• matplotlib - visualizing images and results
• pycocotools - for COCO dataset and evaluation; Windows version is here

There is a problem with pycocotools for Windows. See Issue #356.

Besides, it’s better to remove the prints in pycocotools.

optional:

• nvidia dali (Linux) - a faster data loader

Datasets

This repository supports VOC and COCO datasets.

If you want to train your own dataset, you may:

• write the correponding dataset code
• convert your dataset to COCO-style

PASCAL VOC 2012 (download): http://host.robots.ox.ac.uk/pascal/VOC/voc2012/

MS COCO 2017: http://cocodataset.org/

Nvidia DALI is strongly recommended. It’s much faster than the original data loader.

Currently this repository supports COCO-style dataset with DALI.

Training

Train on COCO dataset, using 1 GPU (if you wanna use 2 GPUs, set --nproc_per_node=2):

python -m torch.distributed.launch --nproc_per_node=1 --use_env train.py --use-cuda --dali --mosaic \
--epochs 190 --data-dir "./data/coco2017" --ckpt-path "yolov5s_coco.pth"

A more concrete modification is in run.sh.

To run it:

bash ./run.sh

If you are using PyTorch ≥ 1.6.0 and RTX series GPUs, the code will enable automatic mixed training (AMP).

Demo and Evaluation

• Run demo.ipynb.
• Modify the parameters in eval.ipynb to test the model.

Performance

Test on COCO 2017 val set, on a single RTX 2080Ti GPU:

The weights is from ultralytics’ repo.

modelbbox APFPSparamsYOLOv5s36.14107.5M

GitHub

#machine learning #pytorch #yolov5 #python

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Buddha Community

A PyTorch implementation of YOLOv5

A PyTorch implementation of YOLOv5.

This repository has two features:

• It is pure python code and can be run immediately using PyTorch 1.4 without build
• Simplified construction and easy to understand how the model works

The model is based on ultralytics’ repo,

and the code is using the structure of TorchVision.

Requirements

• Windows or Linux, with Python ≥ 3.6
• PyTorch** ≥ 1.4.0**
• matplotlib - visualizing images and results
• pycocotools - for COCO dataset and evaluation; Windows version is here

There is a problem with pycocotools for Windows. See Issue #356.

Besides, it’s better to remove the prints in pycocotools.

optional:

• nvidia dali (Linux) - a faster data loader

Datasets

This repository supports VOC and COCO datasets.

If you want to train your own dataset, you may:

• write the correponding dataset code
• convert your dataset to COCO-style

PASCAL VOC 2012 (download): http://host.robots.ox.ac.uk/pascal/VOC/voc2012/

MS COCO 2017: http://cocodataset.org/

Nvidia DALI is strongly recommended. It’s much faster than the original data loader.

Currently this repository supports COCO-style dataset with DALI.

Training

Train on COCO dataset, using 1 GPU (if you wanna use 2 GPUs, set --nproc_per_node=2):

python -m torch.distributed.launch --nproc_per_node=1 --use_env train.py --use-cuda --dali --mosaic \
--epochs 190 --data-dir "./data/coco2017" --ckpt-path "yolov5s_coco.pth"

A more concrete modification is in run.sh.

To run it:

bash ./run.sh

If you are using PyTorch ≥ 1.6.0 and RTX series GPUs, the code will enable automatic mixed training (AMP).

Demo and Evaluation

• Run demo.ipynb.
• Modify the parameters in eval.ipynb to test the model.

Performance

Test on COCO 2017 val set, on a single RTX 2080Ti GPU:

The weights is from ultralytics’ repo.

modelbbox APFPSparamsYOLOv5s36.14107.5M

GitHub

#machine learning #pytorch #yolov5 #python

Implementing Real-time Object Detection System using PyTorch and OpenCV

Hands-On Guide to implement real-time object detection system using python

The Self-Driving car might still be having difficulties understanding the difference between humans and garbage can, but that does not take anything away from the amazing progress state-of-the-art object detection models have made in the last decade.

Combine that with the image processing abilities of libraries like OpenCV, it is much easier today to build a real-time object detection system prototype in hours. In this guide, I will try to show you how to develop sub-systems that go into a simple object detection application and how to put all of that together.

Python vs C++

Reading The Video Stream

Load the Model

Scoring a Single Frame

…

#artificial-intelligence #python #programming #implementing real-time object detection system #implementing real-time object detection system using pytorch and opencv #pytorch

PyTorch For Deep Learning 

What is Pytorch ?

Pytorch is a Deep Learning Library Devoloped by Facebook. it can be used for various purposes such as Natural Language Processing , Computer Vision, etc

Prerequisites

Python, Numpy, Pandas and Matplotlib

Tensor Basics

What is a tensor ?

A Tensor is a n-dimensional array of elements. In pytorch, everything is a defined as a tensor.

#pytorch #pytorch-tutorial #pytorch-course #deep-learning-course #deep-learning

Facebook Gives Away This PyTorch Library For Differential Privacy

Recently, Facebook AI open-sourced a new high-speed library for training PyTorch models with differential privacy (DP) known as Opacus. The library is claimed to be more scalable than existing state-of-the-art methods.

According to the developers at the social media giant, differential privacy is a mathematically rigorous framework for quantifying the anonymisation of sensitive data. With the growing interest in the machine learning (ML) community, this framework is often used in analytics and computations.

Differential privacy constitutes a strong standard for privacy guarantees for algorithms on aggregate databases. It is usually defined in terms of the application-specific concept of adjacent databases. The framework has several properties that make it particularly useful in applications, such as group privacy, robustness to auxiliary information, among others.

#developers corner #differential privacy #facebook ai research #facebook differential privacy #opacus #pytorch #pytorch library #pytorch library opacus

Guide to Conda for TensorFlow and PyTorch

Learn how to set up anaconda environments for different versions of CUDA, TensorFlow, and PyTorch

It’s a real shame that the first experience that most people have with deep learning is having to spend days trying to figure out why the model they downloaded off of GitHub just… won’t… run….

Dependency issues are incredibly common when trying to run an off-the-shelf model. The most problematic of which is needing to have the correct version of CUDA for TensorFlow. TensorFlow has been prominent for a number of years meaning that even new models that are released could use an old version of TensorFlow. This wouldn’t be an issue except that it feels like every version of TensorFlow needs a specific version of CUDA where anything else is incompatible. Sadly, installing multiple versions of CUDA on the same machine can be a real pain!

#machine-learning #pytorch #tensorflow #pytorch