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conda remove --name Python-Keras --all

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conda env create -f python39.yml

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conda create -n Python-keras python=3.6

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# Save the model.
with open('model.tflite', 'wb') as f:
    f.write(tflite_model)

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# Convert the model.
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

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from __future__ import division, print_function
# coding=utf-8
import sys
import os
import glob
import re, glob, os,cv2
import numpy as np
import pandas as pd
import glass_detection
from shutil import copyfile

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# Start the training
!./darknet detector train data/obj.data cfg/yolov4_training.cfg yolov4.conv.137 -dont_show

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!unrar e /mydrive/Colab/yolov4/Glasses.rar -d data/obj

import glob
import os
import re

txt_file_paths = glob.glob(r"data/obj/*.txt")
for i, file_path in enumerate(txt_file_paths):
    # get image size
    with open(file_path, "r") as f_o:

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!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights
!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137

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!echo "Glasses" > data/obj.names
!echo -e 'classes= 1\ntrain  = data/train.txt\nvalid  = data/test.txt\nnames = data/obj.names\nbackup = /mydrive/yolov4_v1' > data/obj.data
!mkdir data/obj