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

Exploration of some patients clinical variables.

GRaNDPapA: Generator of Rad Names from Decent Paper Acronyms

The Dual Memory is build from a simple CNN for the deep memory and Linear Regression fro the fast Memory

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

Testbed of AI Systems Quality Management

Unofficial Alias-Free GAN implementation. Based on rosinality's version with expanded training and inference options.

On the Analysis of French Phonetic Idiosyncrasies for Accent Recognition

This is the official implementation of "One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval".

🌎 The Modern Declarative Data Flow Framework for the AI Empowered Generation.

a spacial-temporal pattern detection system for home automation

Source code of the paper PatchGraph: In-hand tactile tracking with learned surface normals.

VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.

Deep learning model for EEG artifact removal

Deep Federated Learning for Autonomous Driving

Minimal implementation of Denoised Smoothing: A Provable Defense for Pretrained Classifiers in TensorFlow.

StarGAN v2 - Official PyTorch Implementation (CVPR 2020)

RL Algorithms with examples in Python / Pytorch / Unity ML agents

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019). A PyTorch implementation.

Official code of our work, AVATAR: A Parallel Corpus for Java-Python Program Translation.

The code used for the free [email protected] Webinar series on Reinforcement Learning in Finance