Added custom object detection folder with all required files

custom_data/custom.names

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+Insulator

custom_data/custom_object_darknet.py

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+# -*- coding: utf-8 -*-
+"""custom_object_darknet
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1W-Vc-pp7NMYxP7Lzj7YrbeMYsLgxGT_C
+
+Mounting the google drive
+"""
+
+from google.colab import drive
+drive.mount('/content/drive')
+
+"""Navigating to required directory"""
+
+# Commented out IPython magic to ensure Python compatibility.
+# %cd drive
+# %cd MyDrive
+# %cd "Vidrona- Object Detection"
+# %cd darknet
+
+"""Printing  contents of current directory"""
+
+!ls
+
+"""Make command to run makefile"""
+
+# !make
+
+"""Command to give permissions"""
+
+!chmod +x ./darknet
+
+"""Command to run yolov3 train file (original command unmodified)"""
+
+!./darknet detector train custom_data/detector.data custom_data/cfg/yolov3-custom.cfg darknet53.conv.74 -dont_show
+
+"""Command to continue training from a particular weight"""
+
+!./darknet detector train custom_data/detector.data custom_data/cfg/yolov3-custom.cfg backup/yolov3-custom_3600.weights -dont_show
+
+"""Command for prediction-"""
+
+!./darknet detector test custom_data/detector.data custom_data/cfg/yolov3-custom2.cfg backup/yolov3-custom2_2000.weights custom_data/test/test1/DJI_0528_JPG.rf.f271180da4406abec44e765177c81dc8.jpg -dont_show
+from IPython.display import Image
+from google.colab import files
+files.download("predictions.jpg")
+Image('predictions.jpg')
+
+"""Command to download weights"""
+
+# !wget https://pjreddie.com/media/files/yolov3.weights
+
+# !wget https://pjreddie.com/media/files/darknet53.conv.74
+
+"""Code snippet to make train.txt and test.txt files which are required to run model"""
+
+import random
+import os
+import subprocess
+import sys
+
+def split_data_set(image_dir):
+
+    f_val = open("custom_data/test.txt", 'w')
+    f_train = open("custom_data/train.txt", 'w')
+
+    path, dirs, files = next(os.walk(image_dir))
+    data_size = len(files)
+
+    ind = 0
+    data_test_size = int(0.1 * data_size)
+    test_array = random.sample(range(data_size), k=data_test_size)
+
+    for f in os.listdir(image_dir):
+        if(f.split(".")[3] == "jpg"):
+            f_train.write("custom_data/images"+'/'+f+'\n')
+
+split_data_set("custom_data/upload")
+
+WINDOWS_LINE_ENDING = b'\r\n'
+UNIX_LINE_ENDING = b'\n'
+
+sou="custom_data/train.txt"
+file_path = sou
+
+with open(file_path, 'rb') as open_file:
+    content = open_file.read()
+
+content = content.replace(WINDOWS_LINE_ENDING, UNIX_LINE_ENDING)
+
+with open(file_path, 'wb') as open_file:
+    open_file.write(content)
+

custom_data/detector.data

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+classes=1
+train=custom_data/train.txt
+valid=custom_data/test.txt
+names=custom_data/custom.names
+backup=backup/

custom_data/labels/DJI_0012_JPG.rf.0214a043549d470e288a0e7be080710c.txt

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+0 0.43017578125 0.4809027777777778 0.05908203125 0.2421875
+0 0.3251953125 0.5130208333333334 0.14892578125 0.1892361111111111
+0 0.544921875 0.3967013888888889 0.0234375 0.13802083333333334

custom_data/test.txt

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+/content/darknet/custom_data/images/DJI_0011_JPG.rf.ae69c3af8ff170d71d775a99c8e3d5b1.jpg
+/content/darknet/custom_data/images/DJI_0024_JPG.rf.ef832ad5dcf77f947087f263230787e4.jpg
+/content/darknet/custom_data/images/DJI_0179_JPG.rf.bb08572af787380d95ca62b056f4772a.jpg
+/content/darknet/custom_data/images/DJI_0179_JPG.rf.fd6eca573ba33b9cbdac350d27caa8be.jpg
+/content/darknet/custom_data/images/DJI_0181_JPG.rf.7515cd0fe267cf19e134c62d1c8ac084.jpg
+/content/darknet/custom_data/images/DJI_0422_JPG.rf.e1a2b8cd5badad0830c37f690200823e.jpg

custom_data/train.txt

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+custom_data/images/DJI_0012_JPG.rf.0214a043549d470e288a0e7be080710c.jpg