theaiguyscode/yolov3-cloud-tutorial

Everything you need in order to get YOLOv3 up and running in the cloud. Learn to train your custom YOLOv3 object detector in the cloud for free!

yolov3-cloud-tutorial's Issues

How to test this on a video?

I have even tried your video on How to Build an Object Detection Classifier with TensorFlow 2.0 on Windows, where you used detect.py, but in this repo, I couldn't find anything related similar to that file.
Is there any way to implement this model on videos??

can't show predictions

C99 mode problem

Hi, I met the problem when executing "make"

./src/classifier.c:756:9: error: ‘for’ loop initial declarations are only allowed in C99 mode

I have done some google and tried add CFLAGS=-std=c99 or CFLAGS = -Wall -std=c99 at the top of Makefile, but it didn't work.

Maybe you can offer some advice?

saving summary in a file?

how i can save the detection summary and coordinates of the boundary boxes in some file?

errorr

ln: failed to create symbolic link '/mydrive/My Drive': Operation not supported

Custom weights not created in yolov3/backup folder

Hello, I could start the training but weights file is not creating in /mydrive/yolov3/backup/ folder. What could be the reason? It just shows this after the training is completed and I am unable to find these files in the root of darknet.

/yolov3-custom.backup
/yolov3-custom.backup

Evaluation of Model with Metrics

@theAIGuysCode Please share the steps to evaluate the trained model. Request the steps to evaluate the model with same metrics used in Yolov3 paper.

How can I pause training, change the batch size, and resume training using the same loss curve?

video not showing

i run it on colab
when i use detection on video (YouTube.mp4 in data file)
!./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights data/YouTube.mp4
after run it video not showing or playing
any idea how to fix it

testing gray scale images?

Hello, how can I modify this code for gray scale images?
def imShow(path):
import cv2
import matplotlib.pyplot as plt
%matplotlib inline

image = cv2.imread(path)
height, width = image.shape[:2]
resized_image = cv2.resize(image,(3width, 3height), interpolation = cv2.INTER_CUBIC)

fig = plt.gcf()
fig.set_size_inches(18, 10)
plt.axis("off")
plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))
plt.show()