Faster RCNN + inception v2 input size
What is the input size of faster RCNN RPN?
I'm using an object detection API of Tensorflow which is using faster RCNN as region proposal network ( RPN ) and Inception as feature extractor ( according to the config file ). The API is using the online approach in prediction phase and detects every input image singly. however, I'm now trying to feed images to the network in the batch manner by use of Tensorflow dataset API.
as you know for make batch out of the data, firstly we need to resize all of the images to a same size. I think the best way of resizing the images is to resize them exactly to the input size of faster RCNN to avoid duplicate resizing. Now my question is what is the input size of the faster RCNN RPN?
thanks in advance
tensorflow image-processing object-detection tensorflow-datasets
asked Nov 20 at 5:57
Arashsyh
749
add a comment |
What is the input size of faster RCNN RPN?
I'm using an object detection API of Tensorflow which is using faster RCNN as region proposal network ( RPN ) and Inception as feature extractor ( according to the config file ). The API is using the online approach in prediction phase and detects every input image singly. however, I'm now trying to feed images to the network in the batch manner by use of Tensorflow dataset API.
as you know for make batch out of the data, firstly we need to resize all of the images to a same size. I think the best way of resizing the images is to resize them exactly to the input size of faster RCNN to avoid duplicate resizing. Now my question is what is the input size of the faster RCNN RPN?
thanks in advance
tensorflow image-processing object-detection tensorflow-datasets
asked Nov 20 at 5:57
Arashsyh
749
add a comment |
What is the input size of faster RCNN RPN?
I'm using an object detection API of Tensorflow which is using faster RCNN as region proposal network ( RPN ) and Inception as feature extractor ( according to the config file ). The API is using the online approach in prediction phase and detects every input image singly. however, I'm now trying to feed images to the network in the batch manner by use of Tensorflow dataset API.
as you know for make batch out of the data, firstly we need to resize all of the images to a same size. I think the best way of resizing the images is to resize them exactly to the input size of faster RCNN to avoid duplicate resizing. Now my question is what is the input size of the faster RCNN RPN?
thanks in advance
tensorflow image-processing object-detection tensorflow-datasets
asked Nov 20 at 5:57
Arashsyh
749
What is the input size of faster RCNN RPN?
I'm using an object detection API of Tensorflow which is using faster RCNN as region proposal network ( RPN ) and Inception as feature extractor ( according to the config file ). The API is using the online approach in prediction phase and detects every input image singly. however, I'm now trying to feed images to the network in the batch manner by use of Tensorflow dataset API.
as you know for make batch out of the data, firstly we need to resize all of the images to a same size. I think the best way of resizing the images is to resize them exactly to the input size of faster RCNN to avoid duplicate resizing. Now my question is what is the input size of the faster RCNN RPN?
thanks in advance
tensorflow image-processing object-detection tensorflow-datasets
tensorflow image-processing object-detection tensorflow-datasets
asked Nov 20 at 5:57
Arashsyh
749
asked Nov 20 at 5:57
Arashsyh
749
edited Nov 20 at 13:24
asked Nov 20 at 5:57
Arashsyh
749
asked Nov 20 at 5:57
Arashsyh
749
asked Nov 20 at 5:57
Arashsyh
749
749
add a comment |
add a comment |
1 Answer
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It depends on the input resolution which was specified in the pipeline config file, in image_resizer.
For example, for Faster R-CNN over InceptionV2 trained on COCO dataset, see this config file.
The specified resolution is 600x1024.
On a side note, fully convolutional architectures (such as RFCN, SSD, YOLO) don't restrict to a single resolution, i.e. you can apply them on different input resolution without modifying the architecture.
But this doesn't mean that the model will be robust to it if you're training on a single resolution.
answered Nov 20 at 12:13
netanel-sam
28917
Thanks a lot, yeah, after reading the paper I found that you're true and RPN is fully convolutional so there is no specific size for input. But, about 600 and 1024 which are mentioned as min resolution and max resolution in the config file, I think it means the network is on it's the best performance if you feed images with shapes neither greater than 1024 nor smaller than 600. I think it doesn't mean 600 * 1024 input size as you mentioned
– Arashsyh
Nov 24 at 12:02
add a comment |
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1 Answer
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1 Answer
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active
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votes
It depends on the input resolution which was specified in the pipeline config file, in image_resizer.
For example, for Faster R-CNN over InceptionV2 trained on COCO dataset, see this config file.
The specified resolution is 600x1024.
On a side note, fully convolutional architectures (such as RFCN, SSD, YOLO) don't restrict to a single resolution, i.e. you can apply them on different input resolution without modifying the architecture.
But this doesn't mean that the model will be robust to it if you're training on a single resolution.
answered Nov 20 at 12:13
netanel-sam
28917
Thanks a lot, yeah, after reading the paper I found that you're true and RPN is fully convolutional so there is no specific size for input. But, about 600 and 1024 which are mentioned as min resolution and max resolution in the config file, I think it means the network is on it's the best performance if you feed images with shapes neither greater than 1024 nor smaller than 600. I think it doesn't mean 600 * 1024 input size as you mentioned
– Arashsyh
Nov 24 at 12:02
add a comment |
It depends on the input resolution which was specified in the pipeline config file, in image_resizer.
For example, for Faster R-CNN over InceptionV2 trained on COCO dataset, see this config file.
The specified resolution is 600x1024.
On a side note, fully convolutional architectures (such as RFCN, SSD, YOLO) don't restrict to a single resolution, i.e. you can apply them on different input resolution without modifying the architecture.
But this doesn't mean that the model will be robust to it if you're training on a single resolution.
answered Nov 20 at 12:13
netanel-sam
28917
Thanks a lot, yeah, after reading the paper I found that you're true and RPN is fully convolutional so there is no specific size for input. But, about 600 and 1024 which are mentioned as min resolution and max resolution in the config file, I think it means the network is on it's the best performance if you feed images with shapes neither greater than 1024 nor smaller than 600. I think it doesn't mean 600 * 1024 input size as you mentioned
– Arashsyh
Nov 24 at 12:02
add a comment |
It depends on the input resolution which was specified in the pipeline config file, in image_resizer.
For example, for Faster R-CNN over InceptionV2 trained on COCO dataset, see this config file.
The specified resolution is 600x1024.
On a side note, fully convolutional architectures (such as RFCN, SSD, YOLO) don't restrict to a single resolution, i.e. you can apply them on different input resolution without modifying the architecture.
But this doesn't mean that the model will be robust to it if you're training on a single resolution.
answered Nov 20 at 12:13
netanel-sam
28917
It depends on the input resolution which was specified in the pipeline config file, in image_resizer.
For example, for Faster R-CNN over InceptionV2 trained on COCO dataset, see this config file.
The specified resolution is 600x1024.
On a side note, fully convolutional architectures (such as RFCN, SSD, YOLO) don't restrict to a single resolution, i.e. you can apply them on different input resolution without modifying the architecture.
But this doesn't mean that the model will be robust to it if you're training on a single resolution.
answered Nov 20 at 12:13
netanel-sam
28917
answered Nov 20 at 12:13
netanel-sam
28917
answered Nov 20 at 12:13
netanel-sam
28917
answered Nov 20 at 12:13
netanel-sam
28917
28917
Thanks a lot, yeah, after reading the paper I found that you're true and RPN is fully convolutional so there is no specific size for input. But, about 600 and 1024 which are mentioned as min resolution and max resolution in the config file, I think it means the network is on it's the best performance if you feed images with shapes neither greater than 1024 nor smaller than 600. I think it doesn't mean 600 * 1024 input size as you mentioned
– Arashsyh
Nov 24 at 12:02
add a comment |
Thanks a lot, yeah, after reading the paper I found that you're true and RPN is fully convolutional so there is no specific size for input. But, about 600 and 1024 which are mentioned as min resolution and max resolution in the config file, I think it means the network is on it's the best performance if you feed images with shapes neither greater than 1024 nor smaller than 600. I think it doesn't mean 600 * 1024 input size as you mentioned
– Arashsyh
Nov 24 at 12:02
Thanks a lot, yeah, after reading the paper I found that you're true and RPN is fully convolutional so there is no specific size for input. But, about 600 and 1024 which are mentioned as min resolution and max resolution in the config file, I think it means the network is on it's the best performance if you feed images with shapes neither greater than 1024 nor smaller than 600. I think it doesn't mean 600 * 1024 input size as you mentioned
– Arashsyh
Nov 24 at 12:02
Thanks a lot, yeah, after reading the paper I found that you're true and RPN is fully convolutional so there is no specific size for input. But, about 600 and 1024 which are mentioned as min resolution and max resolution in the config file, I think it means the network is on it's the best performance if you feed images with shapes neither greater than 1024 nor smaller than 600. I think it doesn't mean 600 * 1024 input size as you mentioned
– Arashsyh
Nov 24 at 12:02
add a comment |
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