jrieke / shape-detection Goto Github PK
View Code? Open in Web Editor NEW๐ฃ Object detection of abstract shapes with neural networks
License: MIT License
๐ฃ Object detection of abstract shapes with neural networks
License: MIT License
Hello?
Thanks for uploading this and I have a question about having multiple classes in a training image. There could be many classes more than just 2 and the bboxes must contain all class coordinates per a training image.
Ex. train_image_0.jpg: 4 classes
train_image_1.jpg: 1 class
train_image_2.jpg: 10 classes
...
But you defined bboxes to have max. 2 classes per an image.
bboxes = np.zeros((num_imgs, num_objects, 4))
Then how to re-define the bboxes?
Thanks,
In the file ``two-rectangles-or-triangles` you have the following code:
iou = IOU(pred_bboxes[:4], exp_bboxes[:4]) + IOU(pred_bboxes[4:], exp_bboxes[4:])
iou_flipped = IOU(pred_bboxes[:4], flipped_exp_bboxes[:4]) + IOU(pred_bboxes[4:], flipped_exp_bboxes[4:])
dist = IOU(pred_bboxes[:4], exp_bboxes[:4]) + IOU(pred_bboxes[4:], exp_bboxes[4:])
dist_flipped = IOU(pred_bboxes[:4], flipped_exp_bboxes[:4]) + IOU(pred_bboxes[4:], flipped_exp_bboxes[4:])
if mse_flipped < mse: # using iou or dist here leads to similar results
Here, dist
is calculated the same as iou
, which is wrong!a
Output_performance_simple_model.txt
Hi,
I tried your notebook using tensorflow.keras instead of Keras. (Tensorflow-gpu v 2.1.0).
I only modified the code for the model in Cell [81], where I removed the parameter dim_ordering='tf' because it is not needed (and available) in tensorflow.keras.
I tried to train the model for 20 epochs multiple times and I get very different results with respect to your notebook, in terms of "Accuracy shapes" and "Accuracy colors". (see Output_performance_simple_model.txt ). Do you know what can be the problem?
Can it be only the different version of Keras that I used?
Best regards,
Giulia
Hi,
In the single-rectangle.ipynb file, is it able to detect rectangles with for example 40 rotation?
Hi, nice work. came across while looking for such approach.
one question, on what kind of images you have trained your model, that is, you have only small images of that figures or the figures are on entire images? give some hints.
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