YoloV2 full weights loading, loss function and annotation parser #17
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Adapted from "initial_work" branch based on https://fairyonice.github.io/Part_4_Object_Detection_with_Yolo_using_VOC_2012_data_loss.html
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- Coverage 57.29% 37% -20.3%
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+ Hits 157 158 +1
- Misses 117 269 +152
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Hi, Yavuz |
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Hi, Many thanks for the great work! Iulian |
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Great stuff @iuliancioarca ! and @Ybakman awesome to see it training!! I think before we merge this PR, we need to make sure that all tests are passing, and that we're not regressing any existing functionality (or at least if we do there's a decent reason to). For instance, this should still serve the v2-tiny version as well as the v2 full etc. @iuliancioarca do you think you could try to get the CI tests to pass? I can add you as collaborators on the CI platforms so that you can cancel/restart instances if that helps? |
As I work on amazon aws p2x.large machines, memory was not a problem. But you indicate a critical point. I will try to solve it. However, if you want to try it now, in preprocessing.jl write => |
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Thanks! I managed to train a model with reasonable accuracy even with the reduced number of images. |
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Ok, absorbing https://github.com/Ybakman/YoloV2-Trainable makes sense before this PR. @Ybakman would you be able to put together a PR that passes CI tests? I'd do it myself but, I'm afraid I don't have much free time currently |
I've 5 exams in the following 5 days but I can handle that as soon as possible after the final exams. |
Hi,
I managed to generate the full V2 model and to load weights into it (example in Readme). I also worked on the loss function. Basically I tried to continue your work from the 'initial branch'. I had to run in parallel the python code in order to check the array dimensions, otherwise it's a nightmare. There are some differences in the returned values between the two implementations (I think they are related to tf.sparse_softmax_cross_entropy_with_logits), but I only tested on some dummy arrays filled with zeros and ones. What I find strange about the python implementation is that calling the loss function with the same variable as true&predicted array will not return 0 (as I expected).
Anyway, I think the best way to test it is to load some actual data. I also started writing the xml annotation parser for VOC in order to store info in some Julia struct.
Iulian