TFOD-MASKRCNN - Tensorflow MaskRCNN With Python

Related tags

Deep Learning TFOD-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
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