SSD.pytorch: A PyTorch Implementation of Single Shot MultiBox Detector

SSD: Single Shot MultiBox Object Detector, in PyTorch

A PyTorch implementation of Single Shot MultiBox Detector from the 2016 paper by Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang, and Alexander C. Berg. The official and original Caffe code can be found here.

    

Installation

• Install PyTorch by selecting your environment on the website and running the appropriate command.
• Clone this repository.
  • Note: We currently only support Python 3+.
• Then download the dataset by following the instructions below.
• We now support Visdom for real-time loss visualization during training!
  • To use Visdom in the browser:
  • Then (during training) navigate to http://localhost:8097/ (see the Train section below for training details).

# First install Python server and client
pip install visdom
# Start the server (probably in a screen or tmux)
python -m visdom.server

• Note: For training, we currently support VOC and COCO, and aim to add ImageNet support soon.

Datasets

To make things easy, we provide bash scripts to handle the dataset downloads and setup for you. We also provide simple dataset loaders that inherit torch.utils.data.Dataset, making them fully compatible with the torchvision.datasets API.

COCO

Microsoft COCO: Common Objects in Context

Download COCO 2014

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/COCO2014.sh

VOC Dataset

PASCAL VOC: Visual Object Classes

Download VOC2007 trainval & test

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2007.sh # <directory>

Download VOC2012 trainval

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2012.sh # <directory>

Training SSD

• First download the fc-reduced VGG-16 PyTorch base network weights at: https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
• By default, we assume you have downloaded the file in the ssd.pytorch/weights dir:

mkdir weights
cd weights
wget https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth

• To train SSD using the train script simply specify the parameters listed in train.py as a flag or manually change them.

python train.py

• Note:
  • For training, an NVIDIA GPU is strongly recommended for speed.
  • For instructions on Visdom usage/installation, see the Installation section.
  • You can pick-up training from a checkpoint by specifying the path as one of the training parameters (again, see train.py for options)

Evaluation

To evaluate a trained network:

python eval.py

You can specify the parameters listed in the eval.py file by flagging them or manually changing them.

Performance

VOC2007 Test

mAP

Original	Converted weiliu89 weights	From scratch w/o data aug	From scratch w/ data aug
77.2 %	77.26 %	58.12%	77.43 %

FPS

GTX 1060: ~45.45 FPS

Demos

Use a pre-trained SSD network for detection

Download a pre-trained network

• We are trying to provide PyTorch state_dicts (dict of weight tensors) of the latest SSD model definitions trained on different datasets.
• Currently, we provide the following PyTorch models:
  • SSD300 trained on VOC0712 (newest PyTorch weights)
    • https://s3.amazonaws.com/amdegroot-models/ssd300_mAP_77.43_v2.pth
  • SSD300 trained on VOC0712 (original Caffe weights)
    • https://s3.amazonaws.com/amdegroot-models/ssd_300_VOC0712.pth
• Our goal is to reproduce this table from the original paper

Try the demo notebook

• Make sure you have jupyter notebook installed.
• Two alternatives for installing jupyter notebook:

If you installed PyTorch with conda (recommended), then you should already have it. (Just navigate to the ssd.pytorch cloned repo and run): jupyter notebook

If using pip:

# make sure pip is upgraded
pip3 install --upgrade pip
# install jupyter notebook
pip install jupyter
# Run this inside ssd.pytorch
jupyter notebook

• Now navigate to demo/demo.ipynb at http://localhost:8888 (by default) and have at it!

Try the webcam demo

• Works on CPU (may have to tweak cv2.waitkey for optimal fps) or on an NVIDIA GPU
• This demo currently requires opencv2+ w/ python bindings and an onboard webcam
  • You can change the default webcam in demo/live.py
• Install the imutils package to leverage multi-threading on CPU:
  • pip install imutils
• Running python -m demo.live opens the webcam and begins detecting!

TODO

We have accumulated the following to-do list, which we hope to complete in the near future

• Still to come:
  •  Support for the MS COCO dataset
  •  Support for SSD512 training and testing
  •  Support for training on custom datasets

Authors

• Max deGroot
• Ellis Brown

Note: Unfortunately, this is just a hobby of ours and not a full-time job, so we'll do our best to keep things up to date, but no guarantees. That being said, thanks to everyone for your continued help and feedback as it is really appreciated. We will try to address everything as soon as possible.

References

• Wei Liu, et al. "SSD: Single Shot MultiBox Detector." ECCV2016.
• Original Implementation (CAFFE)
• A huge thank you to Alex Koltun and his team at Webyclip for their help in finishing the data augmentation portion.
• A list of other great SSD ports that were sources of inspiration (especially the Chainer repo):
  • Chainer, Keras, MXNet, Tensorflow

Download Details:

Author: Amdegroot
Source Code: https://github.com/amdegroot/ssd.pytorch 
License: MIT license

#machinelearning #computervision #deeplearning #pytorch 

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SSD.pytorch: A PyTorch Implementation of Single Shot MultiBox Detector

SSD: Single Shot MultiBox Object Detector, in PyTorch

A PyTorch implementation of Single Shot MultiBox Detector from the 2016 paper by Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang, and Alexander C. Berg. The official and original Caffe code can be found here.

    

Installation

• Install PyTorch by selecting your environment on the website and running the appropriate command.
• Clone this repository.
  • Note: We currently only support Python 3+.
• Then download the dataset by following the instructions below.
• We now support Visdom for real-time loss visualization during training!
  • To use Visdom in the browser:
  • Then (during training) navigate to http://localhost:8097/ (see the Train section below for training details).

# First install Python server and client
pip install visdom
# Start the server (probably in a screen or tmux)
python -m visdom.server

• Note: For training, we currently support VOC and COCO, and aim to add ImageNet support soon.

Datasets

To make things easy, we provide bash scripts to handle the dataset downloads and setup for you. We also provide simple dataset loaders that inherit torch.utils.data.Dataset, making them fully compatible with the torchvision.datasets API.

COCO

Microsoft COCO: Common Objects in Context

Download COCO 2014

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/COCO2014.sh

VOC Dataset

PASCAL VOC: Visual Object Classes

Download VOC2007 trainval & test

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2007.sh # <directory>

Download VOC2012 trainval

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2012.sh # <directory>

Training SSD

• First download the fc-reduced VGG-16 PyTorch base network weights at: https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
• By default, we assume you have downloaded the file in the ssd.pytorch/weights dir:

mkdir weights
cd weights
wget https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth

• To train SSD using the train script simply specify the parameters listed in train.py as a flag or manually change them.

python train.py

• Note:
  • For training, an NVIDIA GPU is strongly recommended for speed.
  • For instructions on Visdom usage/installation, see the Installation section.
  • You can pick-up training from a checkpoint by specifying the path as one of the training parameters (again, see train.py for options)

Evaluation

To evaluate a trained network:

python eval.py

You can specify the parameters listed in the eval.py file by flagging them or manually changing them.

Performance

VOC2007 Test

mAP

Original	Converted weiliu89 weights	From scratch w/o data aug	From scratch w/ data aug
77.2 %	77.26 %	58.12%	77.43 %

FPS

GTX 1060: ~45.45 FPS

Demos

Use a pre-trained SSD network for detection

Download a pre-trained network

• We are trying to provide PyTorch state_dicts (dict of weight tensors) of the latest SSD model definitions trained on different datasets.
• Currently, we provide the following PyTorch models:
  • SSD300 trained on VOC0712 (newest PyTorch weights)
    • https://s3.amazonaws.com/amdegroot-models/ssd300_mAP_77.43_v2.pth
  • SSD300 trained on VOC0712 (original Caffe weights)
    • https://s3.amazonaws.com/amdegroot-models/ssd_300_VOC0712.pth
• Our goal is to reproduce this table from the original paper

Try the demo notebook

• Make sure you have jupyter notebook installed.
• Two alternatives for installing jupyter notebook:

If you installed PyTorch with conda (recommended), then you should already have it. (Just navigate to the ssd.pytorch cloned repo and run): jupyter notebook

If using pip:

# make sure pip is upgraded
pip3 install --upgrade pip
# install jupyter notebook
pip install jupyter
# Run this inside ssd.pytorch
jupyter notebook

• Now navigate to demo/demo.ipynb at http://localhost:8888 (by default) and have at it!

Try the webcam demo

• Works on CPU (may have to tweak cv2.waitkey for optimal fps) or on an NVIDIA GPU
• This demo currently requires opencv2+ w/ python bindings and an onboard webcam
  • You can change the default webcam in demo/live.py
• Install the imutils package to leverage multi-threading on CPU:
  • pip install imutils
• Running python -m demo.live opens the webcam and begins detecting!

TODO

We have accumulated the following to-do list, which we hope to complete in the near future

• Still to come:
  •  Support for the MS COCO dataset
  •  Support for SSD512 training and testing
  •  Support for training on custom datasets

Authors

• Max deGroot
• Ellis Brown

Note: Unfortunately, this is just a hobby of ours and not a full-time job, so we'll do our best to keep things up to date, but no guarantees. That being said, thanks to everyone for your continued help and feedback as it is really appreciated. We will try to address everything as soon as possible.

References

• Wei Liu, et al. "SSD: Single Shot MultiBox Detector." ECCV2016.
• Original Implementation (CAFFE)
• A huge thank you to Alex Koltun and his team at Webyclip for their help in finishing the data augmentation portion.
• A list of other great SSD ports that were sources of inspiration (especially the Chainer repo):
  • Chainer, Keras, MXNet, Tensorflow

Download Details:

Author: Amdegroot
Source Code: https://github.com/amdegroot/ssd.pytorch 
License: MIT license

#machinelearning #computervision #deeplearning #pytorch 

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.

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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

Tips for Single Shot Object Detectors

Object detection is one of the most central and critical tasks in computer vision. It is also a task with a number of practical benefits. From autonomous driving to surveillance, a well trained object detector can bring a lot of performance advantages to the table.

The recent advances in Deep Learning aided computer vision, driven primarily by the Convolutional Neural Network (CNN) architecture and more recently by the Transformer architecture have produced a number of excellent object detectors at the disposal of a computer vision practitioner. Focusing on the CNNs, a series of models of the two stage approach have been developed. These include Fast R-CNN and Faster R-CNN, two go to designs for practitioners. As the description suggests, these designs require two passes through the image: in the fast pass the network learns to formulate good regions of interest (RoI) and in the second pass the RoIs are linked to the objects to be detected.

As can be imagined, the two pass design makes these designs slower to train, and hence Single Shot Detectors (SSD) were developed that require a single pass through the image. The network performs the tasks of producing regions of interest, called anchor boxes in this design, as well as doing the object classification simultaneously in these designs. Examples of this architecture include SSD, YOLO, RetinaNet and EfficientDet. While the initial single shot detectors were not as accurate, recent revisions have greatly improved the accuracy of these designs, and their faster training times make them highly desirable for practical applications.

The performance of Deep Learning architectures often depends on carefully chosen hyper-parameters, and not surprisingly, the single shot detectors are no exception — in particular, the anchor scales and anchor ratios are prime examples of such parameters. These parameters, along with the image size and shape being used (such as 512x512 or 1024x1024 etc), determine the overall accuracy of the model being trained. Let us look deeper into how we can determine the best values of these for a task. For our example, we will work with the task of detecting helmets of NFL players in images taken at different angles. This dataset was provided as part of the recent NFL 1st and Future Kaggle Challenge. We will use EfficientDet as the model under study. Data is presented for training with compound coefficient 0 (512x512 image) and batch size 4 (due to GPU restrictions).

#pytorch #object-detection #deep-learning #machine-learning #python