I have a dataset which contain both larger objects and very smaller objects . Faster_rcnn is used for training, i got good mAP value for larger objects but for smaller objects the accuracy is very very less…
Which detection model is good for both ? Or should i change any parameters in Faster_rcnn while training for smaller objects ? faster_rcnn_train.txt (3.63 KB)
My Training dataset have 3 objects which is very small and 2 objects which is very large. If i change the anchor size into smaller objects, is it affect the larger object detection?
Image resolution is 4096*2160, where the larger object will have 3/4 size of the image.
Hi samjith888,
You mentioned that “got good mAP value for larger objects but for smaller objects the accuracy is very very less…”, but it conflicts with your tlt-evaluate result.
Hi samjith888,
You set 1024x544 in your training spec file.
size_height_width {
height: 544
width: 1024
}
So,I want to confirm that in your 1024x544 dataset, the objects are small, right?
Could you tell me the average size(height? width?) of the small objects?
I think you can simply check this via your dataset’s labels.
Hi samjith888,
Please try more experiments.
1 Make sure the anchor box size is almost the same as the objects’ size. In your config file,
your anchors are as below. So they can cover the small objects(87/4, 54/4).
But I suggest you to check your small objects’ size further, to see if it is needed to trigger more experiments for different anchor ratio or scale.
3.Try other networks in TLT as well to see if there is any improvement.
try ssd, with lower ratio too.
try detectnet_v2. In dectent_v2, set lower minimum_bounding_box_height(try to set to 3), lower minimum_height and minimum_width (try to set to 0) and lower minimum_detection_ground_truth_overlap (try to set to 0.3)
Yes, you can try.
For detectnet_v2, can you attach your training spec and full training log?
Note: For detectnet_v2 and SSD, all of the images must be resized offline to the final training size and the corresponding bounding boxes must be scaled accordingly.That means if you set 1024544 in the training spec, you need to resize your images offline and you need to modify the bbox(x1,y1,x2,y2) in your label files since you have resized the images from 40962160 to 1024*544.