Training on Tiny Yolo V2

[izesaon]

Hi,

Thanks for the great repository. I am currently building a model to be retrained for my own dataset and wanted to clarify how I should go about retraining it using your repository if the support is provided?

[bastiaanv]

Hi, Thank you for your interest in my repo! As it is now, I do not support retraining in my repo using a automatic system or something like that. You can, however, eddit the weights file to something that would fit better on your needs. I assume you want a system, in which you can put in a lot of images and it would adjusts it's weights and biases. In the YoloV2 system, you have 2 systems build in: 1. Image classification 2. Object location system. I have made, some time ago, code that would make backprop possible for the image classification, but I have not been succesfull in creating the same for the object location system... So as it stands, it is not possible to retrain tge network using your own images/dataset. It is possible to use a different weights file that fits the networks shape and your needs Op di 11 dec. 2018 08:42 schreef Cindy <[email protected]:

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Hi, Thanks for the great repository. I am currently building a model to be retrained for my own dataset and wanted to clarify how I should go about retraining it using your repository if the support is provided? — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#1>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ADzZXKmHpalWPe3zXtrCpaVc5WaKXg6nks5u32H2gaJpZM4ZM1KV> .

[izesaon]

Hi, Does this mean that the weights I generated from retraining yolo tiny v2 on another dataset could be used for your repository?

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On 11 Dec 2018, at 10:58 PM, Bastiaan Verhaar ***@***.***> wrote: Hi, Thank you for your interest in my repo! As it is now, I do not support retraining in my repo using a automatic system or something like that. You can, however, eddit the weights file to something that would fit better on your needs. I assume you want a system, in which you can put in a lot of images and it would adjusts it's weights and biases. In the YoloV2 system, you have 2 systems build in: 1. Image classification 2. Object location system. I have made, some time ago, code that would make backprop possible for the image classification, but I have not been succesfull in creating the same for the object location system... So as it stands, it is not possible to retrain tge network using your own images/dataset. It is possible to use a different weights file that fits the networks shape and your needs Op di 11 dec. 2018 08:42 schreef Cindy ***@***.***: > Hi, > > Thanks for the great repository. I am currently building a model to be > retrained for my own dataset and wanted to clarify how I should go about > retraining it using your repository if the support is provided? > > — > You are receiving this because you are subscribed to this thread. > Reply to this email directly, view it on GitHub > <#1>, or mute the > thread > <https://github.com/notifications/unsubscribe-auth/ADzZXKmHpalWPe3zXtrCpaVc5WaKXg6nks5u32H2gaJpZM4ZM1KV> > . > — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#1 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AP3QrKQ7yoLMsYcJqCWuFjn-gF65Vc80ks5u38gfgaJpZM4ZM1KV>.

[bastiaanv]

Yes it can! I pick the one on the yolo website, but theory ever file generated using the yolov2 tiny network can be loaded into the repo Op di 11 dec. 2018 16:40 schreef Cindy <[email protected]:

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Hi, Does this mean that the weights I generated from retraining yolo tiny v2 on another dataset could be used for your repository? > On 11 Dec 2018, at 10:58 PM, Bastiaan Verhaar ***@***.***> wrote: > > Hi, > > Thank you for your interest in my repo! > > As it is now, I do not support retraining in my repo using a automatic > system or something like that. You can, however, eddit the weights file to > something that would fit better on your needs. > > I assume you want a system, in which you can put in a lot of images and it > would adjusts it's weights and biases. In the YoloV2 system, you have 2 > systems build in: 1. Image classification 2. Object location system. I have > made, some time ago, code that would make backprop possible for the image > classification, but I have not been succesfull in creating the same for the > object location system... > > So as it stands, it is not possible to retrain tge network using your own > images/dataset. It is possible to use a different weights file that fits > the networks shape and your needs > > Op di 11 dec. 2018 08:42 schreef Cindy ***@***.***: > > > Hi, > > > > Thanks for the great repository. I am currently building a model to be > > retrained for my own dataset and wanted to clarify how I should go about > > retraining it using your repository if the support is provided? > > > > — > > You are receiving this because you are subscribed to this thread. > > Reply to this email directly, view it on GitHub > > <#1>, or mute the > > thread > > < https://github.com/notifications/unsubscribe-auth/ADzZXKmHpalWPe3zXtrCpaVc5WaKXg6nks5u32H2gaJpZM4ZM1KV > > > . > > > — > You are receiving this because you authored the thread. > Reply to this email directly, view it on GitHub < #1 (comment)>, or mute the thread < https://github.com/notifications/unsubscribe-auth/AP3QrKQ7yoLMsYcJqCWuFjn-gF65Vc80ks5u38gfgaJpZM4ZM1KV >. > — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#1 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ADzZXAOClJMVuNvEcKhaVkGnawI3LuiAks5u39HdgaJpZM4ZM1KV> .

[izesaon]

Hi, Sorry I am not too sure what you mean. But I suppose that since changing the weights is obtained when I am retraining it on another dataset, your repository would come in handy for me when I do live object detection with the stick as it can identify the objects I trained it for:) The only changes to be done would be the weights, the classes and colors right? Is there any reason for the 8 convolution layers or they are part of the Tiny Yolo V2 Network?

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On 11 Dec 2018, at 11:44 PM, Bastiaan Verhaar ***@***.***> wrote: Yes it can! I pick the one on the yolo website, but theory ever file generated using the yolov2 tiny network can be loaded into the repo Op di 11 dec. 2018 16:40 schreef Cindy ***@***.***: > Hi, > > Does this mean that the weights I generated from retraining yolo tiny v2 > on another dataset could be used for your repository? > > > > On 11 Dec 2018, at 10:58 PM, Bastiaan Verhaar ***@***.***> > wrote: > > > > Hi, > > > > Thank you for your interest in my repo! > > > > As it is now, I do not support retraining in my repo using a automatic > > system or something like that. You can, however, eddit the weights file > to > > something that would fit better on your needs. > > > > I assume you want a system, in which you can put in a lot of images and > it > > would adjusts it's weights and biases. In the YoloV2 system, you have 2 > > systems build in: 1. Image classification 2. Object location system. I > have > > made, some time ago, code that would make backprop possible for the image > > classification, but I have not been succesfull in creating the same for > the > > object location system... > > > > So as it stands, it is not possible to retrain tge network using your own > > images/dataset. It is possible to use a different weights file that fits > > the networks shape and your needs > > > > Op di 11 dec. 2018 08:42 schreef Cindy ***@***.***: > > > > > Hi, > > > > > > Thanks for the great repository. I am currently building a model to be > > > retrained for my own dataset and wanted to clarify how I should go > about > > > retraining it using your repository if the support is provided? > > > > > > — > > > You are receiving this because you are subscribed to this thread. > > > Reply to this email directly, view it on GitHub > > > <#1>, or mute > the > > > thread > > > < > https://github.com/notifications/unsubscribe-auth/ADzZXKmHpalWPe3zXtrCpaVc5WaKXg6nks5u32H2gaJpZM4ZM1KV > > > > > . > > > > > — > > You are receiving this because you authored the thread. > > Reply to this email directly, view it on GitHub < > #1 (comment)>, > or mute the thread < > https://github.com/notifications/unsubscribe-auth/AP3QrKQ7yoLMsYcJqCWuFjn-gF65Vc80ks5u38gfgaJpZM4ZM1KV > >. > > > > — > You are receiving this because you commented. > Reply to this email directly, view it on GitHub > <#1 (comment)>, > or mute the thread > <https://github.com/notifications/unsubscribe-auth/ADzZXAOClJMVuNvEcKhaVkGnawI3LuiAks5u39HdgaJpZM4ZM1KV> > . > — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#1 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AP3QrJXmQ4EcZp8SNC94DLb7gR2gy84Kks5u39LmgaJpZM4ZM1KV>.

[izesaon]

hi, what does

load_conv_layer_bn do? is this section of code specific to the number of classes you have? because when i loaded my pretrained weights, the kernel weights generated using this function returns nan for certain values.

[izesaon]

hi, which part of the code is specific to the weights using darknet? Because when I tried to load my own weights, I get a lot of nan values for prediction but for the simo23 github repository using his code, i didn't get any nan values. Sorry, just wanted to understand the code better as I do not understand every single part.