<p>indraforyou answered the question in how to solve the problem you are having. I want to add something for the inception model specifically. In <a href="https://arxiv.org/pdf/1312.6229.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1312.6229.pdf</a> they propose a regressor network trained on the output of a model trained on the imagenet dataset like the inception model. This regressor model then is used to propose object boundaries for you to use for counting. The advantage of this approach is that you do not have to annotate any training examples and you can just use the ImageNet dataset for training. </p>

<p>If you do not want to train anything I would propose a heuristic in finding object boundaries. Literature in image segmentation <a href="https://en.wikipedia.org/wiki/Image_segmentation" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Image_segmentation</a> should help you find a suitable heuristic. I do think using a heuristic will decrease your accuracy though.</p>

<p>Last but not least this is an open problem in computer vision research. You should not expect to get 100% accuracy or even 95% accuracy on counting. Many very smart people have tried this and reported mixed results. Still some very cool things can be accomplished.</p>
Can inception model be used for object counting in an image?
<p>I have already gone through the image classification part in Inception model, but I require to count the objects in the image. </p>

<p>Considering the flowers data-set, one image can have multiple instances of a flower, so how can I get that count?</p>