How to label overlapping objects for deep learning model training2019 Community Moderator ElectionSpecifying neural network output layout for object detectionHow to generate image using deep learningHow to decide which images to label next?How does YOLO algorithm detect objects if the grid size is way smaller than the object in the test image?Type of images used to train a neural networkHow does the bounding box regressor work in Fast R-CNN?Preparing ground truth labels for YOLO3Bounding Boxes in YOLO ModelTraining detector without bounding box dataYOLO algorithm - understanding training data
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How to label overlapping objects for deep learning model training
2019 Community Moderator ElectionSpecifying neural network output layout for object detectionHow to generate image using deep learningHow to decide which images to label next?How does YOLO algorithm detect objects if the grid size is way smaller than the object in the test image?Type of images used to train a neural networkHow does the bounding box regressor work in Fast R-CNN?Preparing ground truth labels for YOLO3Bounding Boxes in YOLO ModelTraining detector without bounding box dataYOLO algorithm - understanding training data
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I am training yolov3 to detect a custom object (chickens). In a lot of my training images I have overlapping chickens (can only see a partial chicken etc). Is there a common practice for how to label the data (bounding box) in these cases? Should you only label the portion of the image which you can see?
deep-learning labels yolo
asked Mar 21 at 15:08
SjosephSjoseph
1213
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add a comment |
$begingroup$
I am training yolov3 to detect a custom object (chickens). In a lot of my training images I have overlapping chickens (can only see a partial chicken etc). Is there a common practice for how to label the data (bounding box) in these cases? Should you only label the portion of the image which you can see?
deep-learning labels yolo
asked Mar 21 at 15:08
SjosephSjoseph
1213
$endgroup$
add a comment |
$begingroup$
I am training yolov3 to detect a custom object (chickens). In a lot of my training images I have overlapping chickens (can only see a partial chicken etc). Is there a common practice for how to label the data (bounding box) in these cases? Should you only label the portion of the image which you can see?
deep-learning labels yolo
asked Mar 21 at 15:08
SjosephSjoseph
1213
$endgroup$
I am training yolov3 to detect a custom object (chickens). In a lot of my training images I have overlapping chickens (can only see a partial chicken etc). Is there a common practice for how to label the data (bounding box) in these cases? Should you only label the portion of the image which you can see?
deep-learning labels yolo
deep-learning labels yolo
asked Mar 21 at 15:08
SjosephSjoseph
1213
asked Mar 21 at 15:08
SjosephSjoseph
1213
asked Mar 21 at 15:08
SjosephSjoseph
1213
asked Mar 21 at 15:08
SjosephSjoseph
1213
asked Mar 21 at 15:08
SjosephSjoseph
1213
1213
add a comment |
add a comment |
1 Answer
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There is no common practice in labeling the bounding boxes. It is always problem dependent. For example, if you want to count the chickens then you should also label the whole chicken as one instance of a chicken.
If you simply what to detect if there is a chicken in the picture you should label the unoccluded part.
You have to think about your problem. What is the goal of the algorithm? Could a human do the task without imagining where the rest of the object is? You should also consider the pixel imbalance for your problem. In general, the first method is a harder task than the second method because even humans have problems in labeling the bounding box for occluded objects. Hence, you will have a lot of variance due to this factor. If you label only what you see the bounding box labeling will be more reliable. As far as I know, the PASCAL Visual Object Classes data set which was used in the YOLO publication did only label what you can see and not what is occluded.
BTW I hope your task aims to improve the live quality of the chickens. It would be a shame if machine learning would be used to harm them.
answered Mar 22 at 9:41
MachineLearnerMachineLearner
33410
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MachineLearner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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1 Answer
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1 Answer
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$begingroup$
There is no common practice in labeling the bounding boxes. It is always problem dependent. For example, if you want to count the chickens then you should also label the whole chicken as one instance of a chicken.
If you simply what to detect if there is a chicken in the picture you should label the unoccluded part.
You have to think about your problem. What is the goal of the algorithm? Could a human do the task without imagining where the rest of the object is? You should also consider the pixel imbalance for your problem. In general, the first method is a harder task than the second method because even humans have problems in labeling the bounding box for occluded objects. Hence, you will have a lot of variance due to this factor. If you label only what you see the bounding box labeling will be more reliable. As far as I know, the PASCAL Visual Object Classes data set which was used in the YOLO publication did only label what you can see and not what is occluded.
BTW I hope your task aims to improve the live quality of the chickens. It would be a shame if machine learning would be used to harm them.
answered Mar 22 at 9:41
MachineLearnerMachineLearner
33410
New contributor
MachineLearner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
There is no common practice in labeling the bounding boxes. It is always problem dependent. For example, if you want to count the chickens then you should also label the whole chicken as one instance of a chicken.
If you simply what to detect if there is a chicken in the picture you should label the unoccluded part.
You have to think about your problem. What is the goal of the algorithm? Could a human do the task without imagining where the rest of the object is? You should also consider the pixel imbalance for your problem. In general, the first method is a harder task than the second method because even humans have problems in labeling the bounding box for occluded objects. Hence, you will have a lot of variance due to this factor. If you label only what you see the bounding box labeling will be more reliable. As far as I know, the PASCAL Visual Object Classes data set which was used in the YOLO publication did only label what you can see and not what is occluded.
BTW I hope your task aims to improve the live quality of the chickens. It would be a shame if machine learning would be used to harm them.
answered Mar 22 at 9:41
MachineLearnerMachineLearner
33410
New contributor
MachineLearner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
There is no common practice in labeling the bounding boxes. It is always problem dependent. For example, if you want to count the chickens then you should also label the whole chicken as one instance of a chicken.
If you simply what to detect if there is a chicken in the picture you should label the unoccluded part.
You have to think about your problem. What is the goal of the algorithm? Could a human do the task without imagining where the rest of the object is? You should also consider the pixel imbalance for your problem. In general, the first method is a harder task than the second method because even humans have problems in labeling the bounding box for occluded objects. Hence, you will have a lot of variance due to this factor. If you label only what you see the bounding box labeling will be more reliable. As far as I know, the PASCAL Visual Object Classes data set which was used in the YOLO publication did only label what you can see and not what is occluded.
BTW I hope your task aims to improve the live quality of the chickens. It would be a shame if machine learning would be used to harm them.
answered Mar 22 at 9:41
MachineLearnerMachineLearner
33410
New contributor
MachineLearner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
There is no common practice in labeling the bounding boxes. It is always problem dependent. For example, if you want to count the chickens then you should also label the whole chicken as one instance of a chicken.
If you simply what to detect if there is a chicken in the picture you should label the unoccluded part.
You have to think about your problem. What is the goal of the algorithm? Could a human do the task without imagining where the rest of the object is? You should also consider the pixel imbalance for your problem. In general, the first method is a harder task than the second method because even humans have problems in labeling the bounding box for occluded objects. Hence, you will have a lot of variance due to this factor. If you label only what you see the bounding box labeling will be more reliable. As far as I know, the PASCAL Visual Object Classes data set which was used in the YOLO publication did only label what you can see and not what is occluded.
BTW I hope your task aims to improve the live quality of the chickens. It would be a shame if machine learning would be used to harm them.
answered Mar 22 at 9:41
MachineLearnerMachineLearner
33410
New contributor
MachineLearner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited Mar 22 at 9:51
answered Mar 22 at 9:41
MachineLearnerMachineLearner
33410
New contributor
MachineLearner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered Mar 22 at 9:41
MachineLearnerMachineLearner
33410
answered Mar 22 at 9:41
MachineLearnerMachineLearner
33410
33410
New contributor
MachineLearner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
MachineLearner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
MachineLearner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
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