TFOD-MASKRCNN - Tensorflow MaskRCNN With Python

Related tags

Deep LearningTFOD-MASKRCNN
Overview

Tensorflow- MaskRCNN Steps

git clone https://github.com/amalaj7/TFOD-MASKRCNN.git
1.  conda create -n tfod python=3.6   
2.  conda activate tfod  
3.  pip install pillow lxml Cython contextlib2 jupyter matplotlib pandas opencv-python tensorflow==1.15.0 (for GPU- tensorflow-gpu)
4.  conda install -c anaconda protobuf   
5.  go to project path 'models/research'
6.  protoc object_detection/protos/*.proto --python_out=.  
7.  python setup.py install

Install COCO API

8) pip3 install "git+https://github.com/philferriere/cocoapi.git#egg=pycocotools&subdirectory=PythonAPI"

Resize images in a folder

9) python resize_images.py -d train_images/ -s 800 600

Put images and annotations in corresponding folders inside images/ (Annotations are in COCO format)

10)  python create_coco_tf_record.py --logtostderr --train_image_dir=images/train_images --test_image_dir=images/test_images --train_annotations_file=coco_annotations/train.json --test_annotations_file=coco_annotations/test.json --include_masks=True --output_dir=./
  • copy nets and deployment folder and export_inference_graph.py from slim folder and paste it in research folder

Training

  • Create a folder called "training" , inside training folder download your custom model from Model Zoo TF1 | Model Zoo TF2 , extract it and create a labelmap.pbtxt file(sample file is given in training folder) that contains the class labels
  • Alterations in the config file , copy the config file from object_detection/samples/config and paste it in training folder or else u can use the pipeline.config that comes while downloading the pretrained model
  • Edit line no 10 - Number of classes
  • Edit line no 128 - Path to model.ckpt file (downloaded model's file)
  • Edit line no 134 - Iteration
  • Edit line no 143 - path-to-train.record
  • Edit line no 145 and 161 - path-to-labelmap
  • Edit line no 159 - path to test.record

Train model

python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/mask_rcnn_resnet50_atrous_coco.config

Export Tensorflow Graph

python export_inference_graph.py --input_type image_tensor --pipeline_config_path training/mask_rcnn_resnet50_atrous_coco.config --trained_checkpoint_prefix training/model.ckpt-10000 --output_directory my_model_mask

Inference

  • Open object_detection_tutorial.ipynb and replace the necessary fields like model path, config path and test image path

Result

Segmented Result

View tensorboard

tensorboard --logdir=training

Tensorflow2 - MASKRCNN Steps

  • Almost similar steps as above .
git clone https://github.com/tensorflow/models.git
cd models/research
# Compile protos.
protoc object_detection/protos/*.proto --python_out=.
# Install TensorFlow Object Detection API.
cp object_detection/packages/tf2/setup.py .
python -m pip install .

To test the installation

python object_detection/builders/model_builder_tf2_test.py
  • Then follow the above steps from 8 to 10 (includes downloading the pretrained model and editing the config file according to your needs)

Train the model

python model_main_tf2.py --pipeline_config_path=training/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8.config --model_dir=training --alsologtostderr

View tensorboard

tensorboard --logdir=training

Export Tensorflow Graph

python exporter_main_v2.py \
    --trained_checkpoint_dir training/model_checkpoint \
    --output_directory final_model \
    --pipeline_config_path training/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8.config

Inference

  • For TFOD2 , you can utilize inference_from_saved_model_tf2_colab.ipynb and replace the necessary fields like model path, config path and test image path
Owner
Amal Ajay
Goals Matter, But so is the Journey and the Climb.
Amal Ajay
GitHub Repository
A Python reference implementation of the CF data model

cfdm A Python reference implementation of the CF data model. References Compliance with FAIR principles Documentation https://ncas-cms.github.io/cfdm

NCAS CMS 25 Dec 13, 2022
A Python library created to assist programmers with complex mathematical functions

libmaths libmaths was created not only as a learning experience for me, but as a way to make mathematical models in seconds for Python users using mat

Simple 73 Oct 02, 2022
A Python package for generating concise, high-quality summaries of a probability distribution

GoodPoints A Python package for generating concise, high-quality summaries of a probability distribution GoodPoints is a collection of tools for compr

Microsoft 28 Oct 10, 2022
SlotRefine: A Fast Non-Autoregressive Model forJoint Intent Detection and Slot Filling

SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling Reference Main paper to be cited (Di Wu et al., 2020) @article

Moore 34 Nov 03, 2022
Source code for paper "Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling", AAAI 2021

ATLOP Code for AAAI 2021 paper Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling. If you make use of this co

Wenxuan Zhou 146 Nov 29, 2022
A Python library that provides a simplified alternative to DBAPI 2

A Python library that provides a simplified alternative to DBAPI 2. It provides a facade in front of DBAPI 2 drivers.

Tony Locke 44 Nov 17, 2021
Pytorch implementation of Deep Recursive Residual Network for Super Resolution (DRRN)

DRRN-pytorch This is an unofficial implementation of "Deep Recursive Residual Network for Super Resolution (DRRN)", CVPR 2017 in Pytorch. [Paper] You

yun_yang 192 Dec 12, 2022
Testing and Estimation of structural breaks in Stata

xtbreak estimating and testing for many known and unknown structural breaks in time series and panel data. For an overview of xtbreak test see xtbreak

Jan Ditzen 13 Jun 19, 2022
This project helps to colorize grayscale images using multiple exemplars.

Multiple Exemplar-based Deep Colorization (Pytorch Implementation) Pretrained Model [Jitendra Chautharia](IIT Jodhpur)1,3, Prerequisites Python 3.6+ N

jitendra chautharia 3 Aug 05, 2022
The official implementation code of "PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction."

PlantStereo This is the official implementation code for the paper "PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction".

Wang Qingyu 14 Nov 28, 2022
Code for the paper Hybrid Spectrogram and Waveform Source Separation

Demucs Music Source Separation This is the 3rd release of Demucs (v3), featuring hybrid source separation. For the waveform only Demucs (v2): Go this

Meta Research 4.8k Jan 04, 2023
MoveNet Single Pose on DepthAI

MoveNet Single Pose tracking on DepthAI Running Google MoveNet Single Pose models on DepthAI hardware (OAK-1, OAK-D,...). A convolutional neural netwo

64 Dec 29, 2022
alfred-py: A deep learning utility library for **human**

Alfred Alfred is command line tool for deep-learning usage. if you want split an video into image frames or combine frames into a single video, then a

JinTian 800 Jan 03, 2023
JUSTICE: A Benchmark Dataset for Supreme Court’s Judgment Prediction

JUSTICE: A Benchmark Dataset for Supreme Court’s Judgment Prediction CSCI 544 Final Project done by: Mohammed Alsayed, Shaayan Syed, Mohammad Alali, S

Smit Patel 3 Dec 28, 2022
[NeurIPS-2021] Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data

MosaicKD Code for NeurIPS-21 paper "Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data" 1. Motivation Natural images share common l

ZJU-VIPA 37 Nov 10, 2022
2020 CCF大数据与计算智能大赛-非结构化商业文本信息中隐私信息识别-第7名方案

2020CCF-NER 2020 CCF大数据与计算智能大赛-非结构化商业文本信息中隐私信息识别-第7名方案 bert base + flat + crf + fgm + swa + pu learning策略 + clue数据集 = test1单模0.906 词向量

67 Oct 19, 2022
The code for 'Deep Residual Fourier Transformation for Single Image Deblurring'

Deep Residual Fourier Transformation for Single Image Deblurring Xintian Mao, Yiming Liu, Wei Shen, Qingli Li and Yan Wang News 2021.12.5 Release Deep

145 Jan 05, 2023
Contrastively Disentangled Sequential Variational Audoencoder

Contrastively Disentangled Sequential Variational Audoencoder (C-DSVAE) Overview This is the implementation for our C-DSVAE, a novel self-supervised d

Junwen Bai 35 Dec 24, 2022
PrimitiveNet: Primitive Instance Segmentation with Local Primitive Embedding under Adversarial Metric (ICCV 2021)

PrimitiveNet Source code for the paper: Jingwei Huang, Yanfeng Zhang, Mingwei Sun. [PrimitiveNet: Primitive Instance Segmentation with Local Primitive

Jingwei Huang 47 Dec 06, 2022
Official pytorch code for "APP: Anytime Progressive Pruning"

APP: Anytime Progressive Pruning Diganta Misra1,2,3, Bharat Runwal2,4, Tianlong Chen5, Zhangyang Wang5, Irina Rish1,3 1 Mila - Quebec AI Institute,2 L

Landskape AI 12 Nov 22, 2022