Getting inaccurate results using tensorflow net and opencv dnn

edit

@dan01, May I ask you to compare your case with the following one? http://answers.opencv.org/question/18... . Did you use a default *.cofigfile (https://github.com/tensorflow/models/...) with modified number of classes or you changed min_scale / max_scale? Your case is very useful to fix it. Thank you!

Hi @dkurt, thanks for the fast reply. I didn't train the model myself, I just wanted to use it, so I actually didn't use the config file, just the frozen graph. The config file is from here

So here's the diff between the original config and the one on the tensorflow github out.diff

TL;DR:

• num_classes: 1
• anchorwise_output: true - for classification_loss
• batch_size: 6 - in train config
• num_examples: 960 - in eval config
• the paths to model ckpt, train and test records and pbtxt

@dan01, Thank you! The model is correct. All that you need is just use higher confidence threshold (0.2 is now).

Hmm, I don't think it's correct... I've printed the scores and they differ from one model to another (updated images in original post), which led me to try another image. Now i get results similar to this post. Please see the image

.

P.S.: Tensorflow method yields correct results, see here.

@dkurt, setting it to if score > 0.8: yields

the correct box has a score of only 0.26, here's a higher resolution image.

@dan01, besides trying to increase a confidence threshold from 0.2 to 0.6 (if score > 0.2: -> if score > 0.6:), may I ask you to test the first image again but with .png image format but not .jpg? You may convert it using OpenCV's cv.imwrite('/path/to/png', cv.imread('/path/to/jpg')).

@dkurt, done, but the results are the same. Please see below:

if score > 0.2:result1

if score > 0.6:result2

@dan01, I got the problem. This is a deprecated SSD graph since TensorFlow 1.4. You need to modify an every PriorBox_* layer (except PriorBox_0) in the following way: in width and height attribute move the last float_val to be the second one (float_val[0], float_val[1], ..., float_val[N-1] - > float_val[0], float_val[N-1], float_val[1], ..., float_val[N-2]). So it reverts the thing mentioned here.

@dkurt Wow that fixed it! Thank you! So if I re-train the model, this problem shouldn't come up?

@dan01, It's not necessary if you use TensorFlow >= 1.4 and train the model from scratch. However if you wanted to tune this one I think you need to do the same again.