YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4

Related tags

Deep Learningyoltv4
Overview

YOLTv4

Alt text

YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4. YOLTv4 is designed to detect objects in aerial or satellite imagery in arbitrarily large images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks.

This repository is built upon the impressive work of AlexeyAB's YOLOv4 implementation, which improves both speed and detection performance compared to YOLOv3 (which is implemented in SIMRDWN). We use YOLOv4 insead of "YOLOv5", since YOLOv4 is endorsed by the original creators of YOLO, whereas "YOLOv5" is not; furthermore YOLOv4 appears to have superior performance.

Below, we provide examples of how to use this repository with the open-source Rareplanes dataset.


Running YOLTv4


0. Installation

YOLTv4 is built to execute within a docker container on a GPU-enabled machine. The docker command creates an Ubuntu 16.04 image with CUDA 9.2, python 3.6, and conda.

  1. Clone this repository (e.g. to /yoltv4/).

  2. Download model weights to yoltv4/darknet/weights). See: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137 https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-csp.conv.142

  3. Install nvidia-docker.

  4. Build docker file.

     nvidia-docker build -t yoltv4_image /yoltv4/docker
    
  5. Spin up the docker container (see the docker docs for options).

     NV_GPU=0 nvidia-docker run -it -v /local_data:/local_data -v /yoltv4:/yoltv4 -ti --ipc=host --name yoltv4_gpu0 yoltv4_image
    
  6. Compile the Darknet C program.

    First Set GPU=1 CUDNN=1, CUDNN_HALF=1, OPENCV=1 in /yoltv4/darknet/Makefile, then make:

     cd /yoltv4/darknet
     make
    

1. Train

A. Prepare Data

  1. Make YOLO images and labels (see yoltv4/notebooks/train_test_pipeline.ipynb for further details).

  2. Create a txt file listing the training images.

  3. Create file obj.names file with each desired object name on its own line.

  4. Create file obj.data in the directory yoltv4/darknet/data containing necessary files. For example:

    /yoltv4/darknet/data/rareplanes_train.data

     classes = 30
     train =  /local_data/cosmiq/wdata/rareplanes/train/txt/train.txt
     valid =  /local_data/cosmiq/wdata/rareplanes/train/txt/valid.txt
     names =  /yoltv4/darknet/data/rareplanes.name
     backup = backup/
    
  5. Prepare config files.

    See instructions here, or tweak /yoltv4/darknet/cfg/yoltv4_rareplanes.cfg.

B. Execute Training

  1. Execute.

     cd /yoltv4/darknet
     time ./darknet detector train data/rareplanes_train.data  cfg/yoltv4_rareplanes.cfg weights/yolov4.conv.137  -dont_show -mjpeg_port 8090 -map
    
  2. Review progress (plotted at: /yoltv4/darknet/chart_yoltv4_rareplanes.png).


2. Test

A. Prepare Data

  1. Make sliced images (see yoltv4/notebooks/train_test_pipeline.ipynb for further details).

  2. Create a txt file listing the training images.

  3. Create file obj.data in the directory yoltv4/darknet/data containing necessary files. For example:

    /yoltv4/darknet/data/rareplanes_test.data classes = 30 train = valid = /local_data/cosmiq/wdata/rareplanes/test/txt/test.txt names = /yoltv4/darknet/data/rareplanes.name backup = backup/

B. Execute Testing

  1. Execute (proceeds at >80 frames per second on a Tesla P100):

     cd /yoltv4/darknet
     time ./darknet detector valid data/rareplanes_test.data cfg/yoltv4_rareplanes.cfg backup/ yoltv4_rareplanes_best.weights
    
  2. Post-process detections:

    A. Move detections into results directory

     mkdir /yoltv4/darknet/results/rareplanes_preds_v0
     mkdir  /yoltv4/darknet/results/rareplanes_preds_v0/orig_txt
     mv /yoltv4/darknet/results/comp4_det_test_*  /yoltv4/darknet/results/rareplanes_preds_v0/orig_txt/
    

    B. Stitch detections back together and make plots

     time python /yoltv4/yoltv4/post_process.py \
         --pred_dir=/yoltv4/darknet/results/rareplanes_preds_v0/orig_txt/ \
         --raw_im_dir=/local_data/cosmiq/wdata/rareplanes/test/images/ \
         --sliced_im_dir=/local_data/cosmiq/wdata/rareplanes/test/yoltv4/images_slice/ \
         --out_dir= /yoltv4/darknet/results/rareplanes_preds_v0 \
         --detection_thresh=0.25 \
         --slice_size=416} \
         --n_plots=8
    

Outputs will look something like the figures below:

Alt text

Alt text

Alt text

Owner
Adam Van Etten
Adam Van Etten
Implementing DeepMind's Fast Reinforcement Learning paper

Fast Reinforcement Learning This is a repo where I implement the algorithms in the paper, Fast reinforcement learning with generalized policy updates.

Marcus Chiam 6 Nov 28, 2022
NUANCED is a user-centric conversational recommendation dataset that contains 5.1k annotated dialogues and 26k high-quality user turns.

NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions Overview NUANCED is a user-centric conversational recommen

Facebook Research 18 Dec 28, 2021
Grow Function: Generate 3D Stacked Bifurcating Double Deep Cellular Automata based organisms which differentiate using a Genetic Algorithm...

Grow Function: A 3D Stacked Bifurcating Double Deep Cellular Automata which differentiates using a Genetic Algorithm... TLDR;High Def Trees that you can mint as NFTs on Solana

Nathaniel Gibson 4 Oct 08, 2022
ilpyt: imitation learning library with modular, baseline implementations in Pytorch

ilpyt The imitation learning toolbox (ilpyt) contains modular implementations of common deep imitation learning algorithms in PyTorch, with unified in

The MITRE Corporation 11 Nov 17, 2022
This is the repo for our work "Towards Persona-Based Empathetic Conversational Models" (EMNLP 2020)

Towards Persona-Based Empathetic Conversational Models (PEC) This is the repo for our work "Towards Persona-Based Empathetic Conversational Models" (E

Zhong Peixiang 35 Nov 17, 2022
This repository contains FEDOT - an open-source framework for automated modeling and machine learning (AutoML)

package tests docs license stats support This repository contains FEDOT - an open-source framework for automated modeling and machine learning (AutoML

National Center for Cognitive Research of ITMO University 482 Dec 26, 2022
Weakly-supervised semantic image segmentation with CNNs using point supervision

Code for our ECCV paper What's the Point: Semantic Segmentation with Point Supervision. Summary This library is a custom build of Caffe for semantic i

27 Sep 14, 2022
Random Walk Graph Neural Networks

Random Walk Graph Neural Networks This repository is the official implementation of Random Walk Graph Neural Networks. Requirements Code is written in

Giannis Nikolentzos 38 Jan 02, 2023
Pytorch implementation of Compressive Transformers, from Deepmind

Compressive Transformer in Pytorch Pytorch implementation of Compressive Transformers, a variant of Transformer-XL with compressed memory for long-ran

Phil Wang 118 Dec 01, 2022
SemiNAS: Semi-Supervised Neural Architecture Search

SemiNAS: Semi-Supervised Neural Architecture Search This repository contains the code used for Semi-Supervised Neural Architecture Search, by Renqian

Renqian Luo 21 Aug 31, 2022
DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation

DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation By Qing Xu, Wenting Duan and Na He Requirements pytorch==1.1

Qing Xu 20 Dec 09, 2022
Type4Py: Deep Similarity Learning-Based Type Inference for Python

Type4Py: Deep Similarity Learning-Based Type Inference for Python This repository contains the implementation of Type4Py and instructions for re-produ

Software Analytics Lab 45 Dec 15, 2022
Python codes for Lite Audio-Visual Speech Enhancement.

Lite Audio-Visual Speech Enhancement (Interspeech 2020) Introduction This is the PyTorch implementation of Lite Audio-Visual Speech Enhancement (LAVSE

Shang-Yi Chuang 85 Dec 01, 2022
MGFN: Multi-Graph Fusion Networks for Urban Region Embedding was accepted by IJCAI-2022.

Multi-Graph Fusion Networks for Urban Region Embedding (IJCAI-22) This is the implementation of Multi-Graph Fusion Networks for Urban Region Embedding

202 Nov 18, 2022
Codes for building and training the neural network model described in Domain-informed neural networks for interaction localization within astroparticle experiments.

Domain-informed Neural Networks Codes for building and training the neural network model described in Domain-informed neural networks for interaction

DIDACTS 0 Dec 13, 2021
Code for the paper Learning the Predictability of the Future

Learning the Predictability of the Future Code from the paper Learning the Predictability of the Future. Website of the project in hyperfuture.cs.colu

Computer Vision Lab at Columbia University 139 Nov 18, 2022
Generate pixel-style avatars with python.

face2pixel Generate pixel-style avatars with python. Run: Clone the project: git clone https://github.com/theodorecooper/face2pixel install requiremen

Theodore Cooper 2 May 11, 2022
PyTorch Implementation for Deep Metric Learning Pipelines

Easily Extendable Basic Deep Metric Learning Pipeline Karsten Roth ([email 

Karsten Roth 543 Jan 04, 2023
JAXMAPP: JAX-based Library for Multi-Agent Path Planning in Continuous Spaces

JAXMAPP: JAX-based Library for Multi-Agent Path Planning in Continuous Spaces JAXMAPP is a JAX-based library for multi-agent path planning (MAPP) in c

OMRON SINIC X 24 Dec 28, 2022
Code for paper Adaptively Aligned Image Captioning via Adaptive Attention Time

Adaptively Aligned Image Captioning via Adaptive Attention Time This repository includes the implementation for Adaptively Aligned Image Captioning vi

Lun Huang 45 Aug 27, 2022