TFOD-MASKRCNN - Tensorflow MaskRCNN With Python

Last update: Jan 18, 2022

Related tags

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

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

TFOD-MASKRCNN - Tensorflow MaskRCNN With Python

Related tags

Overview

Tensorflow- MaskRCNN Steps

Install COCO API

Resize images in a folder

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

Training

Train model

Export Tensorflow Graph

Inference

Result

View tensorboard

Tensorflow2 - MASKRCNN Steps

To test the installation

Train the model

View tensorboard

Export Tensorflow Graph

Inference

Owner

Amal Ajay

The implementation of 'Image synthesis via semantic composition'.

Pytorch implementation of "Geometrically Adaptive Dictionary Attack on Face Recognition" (WACV 2022)

Minimalistic PyTorch training loop

Learning-based agent for Google Research Football

An implementation of the WHATWG URL Standard in JavaScript

Code for "Offline Meta-Reinforcement Learning with Advantage Weighting" [ICML 2021]

Material for my PyConDE & PyData Berlin 2022 Talk "5 Steps to Speed Up Your Data-Analysis on a Single Core"

Buffon’s needle: one of the oldest problems in geometric probability

Adaptive Pyramid Context Network for Semantic Segmentation (APCNet CVPR'2019)

CVPR 2022 "Online Convolutional Re-parameterization"

POT : Python Optimal Transport

A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading

Official repo for the work titled "SharinGAN: Combining Synthetic and Real Data for Unsupervised GeometryEstimation"

Implementation for "Conditional entropy minimization principle for learning domain invariant representation features"

【steal piano】GitHub偷情分析工具！

A stock generator that assess a list of stocks and returns the best stocks for investing and money allocations based on users choices of volatility, duration and number of stocks

A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution (CVPR2022)

Code associated with the paper "Towards Understanding the Data Dependency of Mixup-style Training".

Python package for missing-data imputation with deep learning

Official PyTorch Implementation of Learning Architectures for Binary Networks