YOLO v3 complete architecture
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I am attempting to implement YOLO v3 in Tensorflow-Keras from scratch, with the aim of training my own model on a custom dataset. By that, I mean without using pretrained weights. I have gone through all three papers for YOLOv1, YOLOv2(YOLO9000) and YOLOv3, and find that although Darknet53 is used as a feature extractor for YOLOv3, I am unable to point out the complete architecture which extends after that - the "detection" layers talked about here. After a lot of reading on blog posts from Medium, kdnuggets and other similar sites, I ended up with a few significant questions:
- Have I have missed the complete architecture of the detection layers (that extend after Darknet53 used for feature extraction) in YOLOv3 paper somewhere?
- The author seems to use different image sizes at different stages of training. Does the network automatically do this upscaling/downscaling of images?
- For preprocessing the images, is it really just enough to resize them and then normalize it (dividing by 255)?
Please be kind enough to point me in the right direction. I appreciate the help!
keras tensorflow object-detection object-recognition yolo
asked 6 mins ago
hridaynshridayns
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I am attempting to implement YOLO v3 in Tensorflow-Keras from scratch, with the aim of training my own model on a custom dataset. By that, I mean without using pretrained weights. I have gone through all three papers for YOLOv1, YOLOv2(YOLO9000) and YOLOv3, and find that although Darknet53 is used as a feature extractor for YOLOv3, I am unable to point out the complete architecture which extends after that - the "detection" layers talked about here. After a lot of reading on blog posts from Medium, kdnuggets and other similar sites, I ended up with a few significant questions:
- Have I have missed the complete architecture of the detection layers (that extend after Darknet53 used for feature extraction) in YOLOv3 paper somewhere?
- The author seems to use different image sizes at different stages of training. Does the network automatically do this upscaling/downscaling of images?
- For preprocessing the images, is it really just enough to resize them and then normalize it (dividing by 255)?
Please be kind enough to point me in the right direction. I appreciate the help!
keras tensorflow object-detection object-recognition yolo
asked 6 mins ago
hridaynshridayns
1012
New contributor
hridayns is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I am attempting to implement YOLO v3 in Tensorflow-Keras from scratch, with the aim of training my own model on a custom dataset. By that, I mean without using pretrained weights. I have gone through all three papers for YOLOv1, YOLOv2(YOLO9000) and YOLOv3, and find that although Darknet53 is used as a feature extractor for YOLOv3, I am unable to point out the complete architecture which extends after that - the "detection" layers talked about here. After a lot of reading on blog posts from Medium, kdnuggets and other similar sites, I ended up with a few significant questions:
- Have I have missed the complete architecture of the detection layers (that extend after Darknet53 used for feature extraction) in YOLOv3 paper somewhere?
- The author seems to use different image sizes at different stages of training. Does the network automatically do this upscaling/downscaling of images?
- For preprocessing the images, is it really just enough to resize them and then normalize it (dividing by 255)?
Please be kind enough to point me in the right direction. I appreciate the help!
keras tensorflow object-detection object-recognition yolo
asked 6 mins ago
hridaynshridayns
1012
New contributor
hridayns is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am attempting to implement YOLO v3 in Tensorflow-Keras from scratch, with the aim of training my own model on a custom dataset. By that, I mean without using pretrained weights. I have gone through all three papers for YOLOv1, YOLOv2(YOLO9000) and YOLOv3, and find that although Darknet53 is used as a feature extractor for YOLOv3, I am unable to point out the complete architecture which extends after that - the "detection" layers talked about here. After a lot of reading on blog posts from Medium, kdnuggets and other similar sites, I ended up with a few significant questions:
- Have I have missed the complete architecture of the detection layers (that extend after Darknet53 used for feature extraction) in YOLOv3 paper somewhere?
- The author seems to use different image sizes at different stages of training. Does the network automatically do this upscaling/downscaling of images?
- For preprocessing the images, is it really just enough to resize them and then normalize it (dividing by 255)?
Please be kind enough to point me in the right direction. I appreciate the help!
keras tensorflow object-detection object-recognition yolo
keras tensorflow object-detection object-recognition yolo
asked 6 mins ago
hridaynshridayns
1012
New contributor
hridayns is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 mins ago
hridaynshridayns
1012
New contributor
hridayns is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 mins ago
hridaynshridayns
1012
New contributor
hridayns is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 mins ago
hridaynshridayns
1012
asked 6 mins ago
hridaynshridayns
1012
1012
New contributor
hridayns is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
hridayns is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
hridayns is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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