<jupyter_start><jupyter_text>SetupBefore you can run the tutorials, there are several things that you need to do Test if you have Tensorflow 2, numpy and matplolib. You must have the following packages installed<jupyter_code>import tensorflow as tf
import tensorflow.keras as tfk 
import numpy as np 
import matplotlib
import os
print(tf.__version__)
print(np.__version__)
print(matplotlib.__version__)<jupyter_output>2.1.0
1.18.5
3.1.2<jupyter_text>Download the pretrained yolo v2 model The inventor of Yolo has a website listing all variations of Yolo V2 model. [Check it out](https://pjreddie.com/darknet/yolov2/)!We are going to use the **YOLOv2	VOC 2007+2012** model, which is trained on the Pascal VOC dataset. Download the model and put it in the **root directory of the repo** (Otherwise you might need to change the path variables in the tutorials)1. Yolo V2: https://pjreddie.com/media/files/yolov2-voc.weights2. Pretrained Darknet 19 448 model: http://pjreddie.com/media/files/darknet19_448.weights (use for transfer learning)  Convert the yolo v2 weights to tensorlfow .h5 file<jupyter_code>CONFIG_PATH = 'Metadata/PascalVoc12/yolov2Voc.cfg' # a file that describes the yolo architecture. Downloaded from the yolo website
WEIGHT_PATH = 'yolov2-voc.weights' # the file you just downloaded
from YoloBackbone.modelBuilder import YOLO2
builder = YOLO2(configPath=CONFIG_PATH, weightPath=WEIGHT_PATH, inputShape=None)
model, anchors, classNum = builder.buildModel() # now we have a tensorflow model, don't worry about the other two for now
#save the tensorflow model in the root directory
model.save('yolov2_voc.h5')<jupyter_output><empty_output>
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{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '266845', 'license': 'MIT', 'n_tokens_mistral': 548, 'n_tokens_neox': 522, 'n_words': 192, 'path': 'Tutorial_0_Setup.ipynb', 'repo_name': 'HeeyoungJoe/YOLO_Explained', 'size': '1653'}