The readme has a small section about how to tweak BadgerDB to consume less memory. However, the section doesn't really describe any of the parameters in depth or allow the reader to be able to make an educated guess on how much memory his/her BadgerDB instance will consume.

The reason I'm bringing this up is that we're heavily using BadgerDB in our software but the options around it feel like a

Per my understanding, the functions bunched together in the sub-directory tf_extended are meant to supplement the SSD implementation using standard TensorFlow functions, but it is not the same as TFX - Tensorflow Extended. Is this correct?

If that's the case, perhaps a modification to the readme will help newcomers avoid conflating the two. I'm willing to

Damn. Is there any one who can clearly explain below two lines???
link

matches = truths[best_truth_idx]          # Shape: [num_priors,4]
conf = labels[best_truth_idx] + 1         # Shape: [num_priors]

I am Android engineer,just follow tutorial "https://docs.opencv.org/3.4.0/d0/d6c/tutorial_dnn_android.html" linked there to find "MobileNetSSD_deploy.caffemodel" file but there is another one ; when i use the file "MobileNet_deploy.caffemodel"
the output is not work as expected

Hi Lufficc,
Thank you for your great work!

May I know as a new learner, is there any more easy understand setup tutorial for the my_dataset.py and the rest of the file configure?
Because I'm trying to reproduce your code in my work station. but your Develop Guide for me is a bit confusing.

Looking forward for your reply. Thank you so much

To further reduce storage space we can make it optional to keep track of the childCount of nodes.

box_coords = np.array([int(x) for x in

。。。。。
ulc_x, ulc_y, lrc_x, lrc_y = box_coords
ValueError: not enough values to unpack (expected 4, got 0)

Mac OSX 10.14.1 (18B75)

Traceback (most recent call last):
File "yolo_opencv.py", line 98, in
draw_prediction(image, class_ids[i], confidences[i], round(x), round(y), round(x+w), round(y+h))
File "yolo_opencv.py", line 39, in draw_prediction
cv2.rectangle(img, (x,y), (x_plus_w,y_plus_h), color, 2)
TypeError: only length-1 arrays can be converted to Python scalars