Closed ghost closed 7 years ago
Hi, If I understand correctly, you are referring to the high-level experiments of the journal version of COB. This process does not require GPU. It also does not require proposals. We snap the results of Semantic Segmentation to the superpixels generated by COB, using simple majority voting (i.e. the class that overlaps the most with the superpixel is assigned to it).
Please correct me if I misunderstood your question.
You understood perfectly. I am interested in getting semantic segments using deepmask for a subset of MSCOCO images. I tried accessing the pre-computed proposals but couldn't go far. I couldn't download and re-run the deepmask algorithm since it needs GPU, Caffee or some other deep learning toolbox. Correct me if I am wrong about this!! Is there a CPU way to get the results as shown in the journal, as in download and run it out of the box on the MSCOCO images?
Appreciate the response. Thanks
For DeepMask, we downloaded the pre-computed proposals from here: https://github.com/facebookresearch/deepmask. However, we used them only for evaluation and not coupled with the pipeline of COB in the object proposals section.
We used snapping to COB superpixels for semantic segmentation instead. This does not require GPU once you have the pre-computed superpixels and semantic segmentation results.
I hope this helps.
I am sorry if I confused you. I am totally new to deep learning and therefore deepmask. I am working on semantic alignment for which I need good segmentation results for images.Here are the questions I have:
I really appreciate the prompt and helpful response. Thanks
Hope this helps :)
okay, thanks a lot. I appreciate your help.
Hi, Thanks for making the toolbox available. I was wondering if there is a way to go from the pre-computed proposals on MSCOCO to high-level meaningful segmentation as shown in your paper (but for some other dataset). Do you need caffee and GPU for that? Sorry I am fairly new to this field!
Thanks