An implementation of RetinaNet in PyTorch.

Related tags

Deep Learningpytorchobject-detectionretinanet
Overview

RetinaNet

An implementation of RetinaNet in PyTorch.

RetinaNet Structure

Installation

  1. Install PyTorch and torchvision.
  2. For faster data augmentation, install pillow-simd:
pip uninstall -y pillow
pip install pillow-simd

Training

COCO 2017

  1. First, install pycocotools:
git clone https://github.com/pdollar/coco/
cd coco/PythonAPI
make
python setup.py install
cd ../..
rm -r coco
  1. Then download COCO 2017 into ./datasets/COCO/:
cd datasets
mkdir COCO
cd COCO

If your using wget:

wget http://images.cocodataset.org/zips/train2017.zip &&
wget http://images.cocodataset.org/zips/val2017.zip &&
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip

If your using aria2c (recommended on for higher bandwidth connections and for allowing resumption of the download. Tune the number of max concurrent downloads (-j) and max connections per server (-x) as needed:

aria2c -x 10 -j 10 http://images.cocodataset.org/zips/train2017.zip &&
aria2c -x 10 -j 10 http://images.cocodataset.org/zips/val2017.zip &&
aria2c -x 10 -j 10 http://images.cocodataset.org/annotations/annotations_trainval2017.zip

unzip *.zip
rm *.zip

Then just run:

python train_coco.py

Pascal VOC

cd datasets
mkdir VOC
cd VOC
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar &&
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar &&
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar

If your using aria2c (recommended on for higher bandwidth connections and for allowing resumption of the download. Tune the number of max concurrent downloads (-j) and max connections per server (-x) as needed:

aria2c -x 10 -j 10 http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar &&
aria2c -x 10 -j 10 http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar &&
aria2c -x 10 -j 10 http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar

tar xf *.tar
rm *.tar

Then just run:

python train_voc.py

Custom Dataset

Lots to write here. 😉

Evaluation

To evaluate an image on a trained model:

python eval.py [checkpoint_path] [image_path]

This will create an image (output.jpg) with bounding box annotations.

Todo

  1. Finish converting the COCO dataset class to work with batches.
  2. Train COCO 2017 for 90,000 iterations and save a reusable checkpoint.
  3. Try training on Pascal VOC and add download instructions.
  4. Produce bounding box outputs for a few sanity check images.
  5. Upload trained weights to Github releases.
  6. Train on the 🔮 magic proprietary dataset ✨ .

Credits

Owner
Conner Vercellino
Interested in NLP, metalearning and other random machine learning topics.
Conner Vercellino
GitHub Repository
Tensor-Based Quantum Machine Learning

TensorLy_Quantum TensorLy-Quantum is a Python library for Tensor-Based Quantum Machine Learning that builds on top of TensorLy and PyTorch. Website: h

TensorLy 85 Dec 03, 2022
Neural Network Libraries

Neural Network Libraries Neural Network Libraries is a deep learning framework that is intended to be used for research, development and production. W

Sony 2.6k Dec 30, 2022
When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings

When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings This is the repository for t

RegLab 39 Jan 07, 2023
ProjectOxford-ClientSDK - This repo has moved :house: Visit our website for the latest SDKs & Samples

This project has moved 🏠 We heard your feedback! This repo has been deprecated and each project has moved to a new home in a repo scoped by API and p

Microsoft 970 Nov 28, 2022
Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation, available for both PyTorch and Tensorflow.

Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation, available for both PyTorch and Tensorflow.

730 Jan 09, 2023
Cluttered MNIST Dataset

Cluttered MNIST Dataset A setup script will download MNIST and produce mnist/*.t7 files: luajit download_mnist.lua Example usage: local mnist_clutter

DeepMind 50 Jul 12, 2022
PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer.

Unsupervised_IEPGAN This is the PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer. Ha

25 Oct 26, 2022
neural image generation

pixray Pixray is an image generation system. It combines previous ideas including: Perception Engines which uses image augmentation and iteratively op

dribnet 398 Dec 17, 2022
Implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"

SinGAN This is an unofficial implementation of SinGAN from someone who's been sitting right next to SinGAN's creator for almost five years. Please ref

35 Nov 10, 2022
g9.py - Torch interactive graphics

g9.py - Torch interactive graphics A Torch toy in the browser. Demo at https://srush.github.io/g9py/ This is a shameless copy of g9.js, written in Pyt

Sasha Rush 13 Nov 16, 2022
PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

Self-Supervised Vision Transformers with DINO PyTorch implementation and pretrained models for DINO. For details, see Emerging Properties in Self-Supe

Facebook Research 4.2k Jan 03, 2023
How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach

EV-charging-impact This repository contains the code that has been used for the Queue modelling for the paper "How will electric vehicles affect traff

7 Nov 30, 2022
Self-Supervised Learning with Kernel Dependence Maximization

Self-Supervised Learning with Kernel Dependence Maximization This is the code for SSL-HSIC, a self-supervised learning loss proposed in the paper Self

DeepMind 29 Dec 29, 2022
Official Code Release for "TIP-Adapter: Training-free clIP-Adapter for Better Vision-Language Modeling"

Official Code Release for "TIP-Adapter: Training-free clIP-Adapter for Better Vision-Language Modeling" Pipeline of Tip-Adapter Tip-Adapter can provid

peng gao 187 Dec 28, 2022
torchbearer: A model fitting library for PyTorch

Note: We're moving to PyTorch Lightning! Read about the move here. From the end of February, torchbearer will no longer be actively maintained. We'll

631 Jan 04, 2023
TreeSubstitutionCipher - Encryption system based on trees and substitution

Tree Substitution Cipher Generation Algorithm: Generate random tree. Tree nodes

stepa 1 Jan 08, 2022
SCI-AIDE : High-fidelity Few-shot Histopathology Image Synthesis for Rare Cancer Diagnosis

SCI-AIDE : High-fidelity Few-shot Histopathology Image Synthesis for Rare Cancer Diagnosis Pretrained Models In this work, we created synthetic tissue

Emirhan Kurtuluş 1 Feb 07, 2022
Learning Representational Invariances for Data-Efficient Action Recognition

Learning Representational Invariances for Data-Efficient Action Recognition Official PyTorch implementation for Learning Representational Invariances

Virginia Tech Vision and Learning Lab 27 Nov 22, 2022
Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems

ACSC Automatic extrinsic calibration for non-repetitive scanning solid-state LiDAR and camera systems. System Architecture 1. Dependency Tested with U

KINO 192 Dec 13, 2022
CTC segmentation python package

CTC segmentation CTC segmentation can be used to find utterances alignments within large audio files. This repository contains the ctc-segmentation py

Ludwig Kürzinger 217 Jan 04, 2023