Mixing
False positives in faster-rcnn object detection
I'm training an object detector using tensorflow and the faster_rcnn_inception_v2_coco model and am experiencing a lot of false positives when classifying on a video.
After some research I've figured out that I need to add negative images to the training process.
How do I add these to tfrecord files? I used the csv to tfrecord file code provided in the tutorial here.
Also it seems that ssd has a hard_example_miner in the config that allows to configure this behaviour but this doesn't seem to be the case for faster rcnn? Is there a way to achieve something similar on faster rcnn?
tensorflow deep-learning object-detection
edited Jan 4 at 13:41
Nick Larsen♦
14.7k65585
asked Jan 2 at 10:00
RepulsrRepulsr
62
add a comment |
I'm training an object detector using tensorflow and the faster_rcnn_inception_v2_coco model and am experiencing a lot of false positives when classifying on a video.
After some research I've figured out that I need to add negative images to the training process.
How do I add these to tfrecord files? I used the csv to tfrecord file code provided in the tutorial here.
Also it seems that ssd has a hard_example_miner in the config that allows to configure this behaviour but this doesn't seem to be the case for faster rcnn? Is there a way to achieve something similar on faster rcnn?
tensorflow deep-learning object-detection
edited Jan 4 at 13:41
Nick Larsen♦
14.7k65585
asked Jan 2 at 10:00
RepulsrRepulsr
62
add a comment |
I'm training an object detector using tensorflow and the faster_rcnn_inception_v2_coco model and am experiencing a lot of false positives when classifying on a video.
After some research I've figured out that I need to add negative images to the training process.
How do I add these to tfrecord files? I used the csv to tfrecord file code provided in the tutorial here.
Also it seems that ssd has a hard_example_miner in the config that allows to configure this behaviour but this doesn't seem to be the case for faster rcnn? Is there a way to achieve something similar on faster rcnn?
tensorflow deep-learning object-detection
edited Jan 4 at 13:41
Nick Larsen♦
14.7k65585
asked Jan 2 at 10:00
RepulsrRepulsr
62
I'm training an object detector using tensorflow and the faster_rcnn_inception_v2_coco model and am experiencing a lot of false positives when classifying on a video.
After some research I've figured out that I need to add negative images to the training process.
How do I add these to tfrecord files? I used the csv to tfrecord file code provided in the tutorial here.
Also it seems that ssd has a hard_example_miner in the config that allows to configure this behaviour but this doesn't seem to be the case for faster rcnn? Is there a way to achieve something similar on faster rcnn?
tensorflow deep-learning object-detection
tensorflow deep-learning object-detection
edited Jan 4 at 13:41
Nick Larsen♦
14.7k65585
asked Jan 2 at 10:00
RepulsrRepulsr
62
edited Jan 4 at 13:41
Nick Larsen♦
14.7k65585
asked Jan 2 at 10:00
RepulsrRepulsr
62
edited Jan 4 at 13:41
Nick Larsen♦
14.7k65585
edited Jan 4 at 13:41
Nick Larsen♦
14.7k65585
edited Jan 4 at 13:41
Nick Larsen♦
14.7k65585
14.7k65585
asked Jan 2 at 10:00
RepulsrRepulsr
62
asked Jan 2 at 10:00
RepulsrRepulsr
62
asked Jan 2 at 10:00
RepulsrRepulsr
62
62
add a comment |
add a comment |
1 Answer
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I was facing the same issue with faster RCNN, although you cannot actually use hard_example_miner with the faster RCNN model, you can add some background images, ie. images with no objects (Everything remains the same, except there is not object tag in the xml for that particular picture)
One more thing that actually worked wonders for me was using the imgaug library, you can augment the images and the bounding boxes using the same script. Try and increase the training data by 10 or 15 times, and then I would suggest you to train again to around 150000-200000 steps.
These two steps helped me reduce the number of false positives effectively.
answered Jan 10 at 5:56
Saransh AgarwalSaransh Agarwal
111
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
I was facing the same issue with faster RCNN, although you cannot actually use hard_example_miner with the faster RCNN model, you can add some background images, ie. images with no objects (Everything remains the same, except there is not object tag in the xml for that particular picture)
One more thing that actually worked wonders for me was using the imgaug library, you can augment the images and the bounding boxes using the same script. Try and increase the training data by 10 or 15 times, and then I would suggest you to train again to around 150000-200000 steps.
These two steps helped me reduce the number of false positives effectively.
answered Jan 10 at 5:56
Saransh AgarwalSaransh Agarwal
111
add a comment |
I was facing the same issue with faster RCNN, although you cannot actually use hard_example_miner with the faster RCNN model, you can add some background images, ie. images with no objects (Everything remains the same, except there is not object tag in the xml for that particular picture)
One more thing that actually worked wonders for me was using the imgaug library, you can augment the images and the bounding boxes using the same script. Try and increase the training data by 10 or 15 times, and then I would suggest you to train again to around 150000-200000 steps.
These two steps helped me reduce the number of false positives effectively.
answered Jan 10 at 5:56
Saransh AgarwalSaransh Agarwal
111
add a comment |
I was facing the same issue with faster RCNN, although you cannot actually use hard_example_miner with the faster RCNN model, you can add some background images, ie. images with no objects (Everything remains the same, except there is not object tag in the xml for that particular picture)
One more thing that actually worked wonders for me was using the imgaug library, you can augment the images and the bounding boxes using the same script. Try and increase the training data by 10 or 15 times, and then I would suggest you to train again to around 150000-200000 steps.
These two steps helped me reduce the number of false positives effectively.
answered Jan 10 at 5:56
Saransh AgarwalSaransh Agarwal
111
I was facing the same issue with faster RCNN, although you cannot actually use hard_example_miner with the faster RCNN model, you can add some background images, ie. images with no objects (Everything remains the same, except there is not object tag in the xml for that particular picture)
One more thing that actually worked wonders for me was using the imgaug library, you can augment the images and the bounding boxes using the same script. Try and increase the training data by 10 or 15 times, and then I would suggest you to train again to around 150000-200000 steps.
These two steps helped me reduce the number of false positives effectively.
answered Jan 10 at 5:56
Saransh AgarwalSaransh Agarwal
111
answered Jan 10 at 5:56
Saransh AgarwalSaransh Agarwal
111
answered Jan 10 at 5:56
Saransh AgarwalSaransh Agarwal
111
answered Jan 10 at 5:56
Saransh AgarwalSaransh Agarwal
111
111
add a comment |
add a comment |
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