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Object-detction-With-TensorFlow

Wheat-Head-Detction

STEP-1

-: dataset-:https://www.kaggle.com/c/global-wheat-detection/data?select=train

-: download-> https://github.com/tensorflow/models

-: filter you image data

-: if there is no BBOX in csv for images then remove those images from dataset

-: csv manipulation

-: now you have (filterd images + csv)

-: create Train.csv and Test.csv

-: create train and test image folder

-: create train.record and test.record

-: I set num_steps: 4310 in "faster_rcnn_resnet50_pets.config" you can change it(initially it is 200000). Try to iterate
more for proper accuracy

-: path to get this file "/models-master/research/object_detection/samples/configs/faster_rcnn_resnet50_pets.config"

-: train your model

-: MAKE SURE, "SET PATH" WHENEVER IT REQUIRES

-: Use this "sys.path.append("../slim")'

-: start training, it will take lots of time

-: Export Graph with "export_inference_graph.py", run this file from your Terminal, Don't forget to put path in the file

STEP-2

-:Use saved .pb file

-:Load it.

-:create rectangle wherever it is detected using OpenCv

-:create CSV for test images (I havn't done this, you just need to dump it into dataframe and then .CSV)

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