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.
Hi samjith888,
As mentioned previously, you need to trigger experiments.To improve accuracy on small objects, the most common trick is to use a smaller set of anchors. The anchor sizes should have a size that is similar to the small objects’ size. Anchor ratios can be kept unchanged.
You can also train only two classes firstly instead of 5 classes. Trigger less classes in order to narrow down.
Mssing_P 25x23
Extra_P 26X13
Note that for above size, since you change from 40962160 to 1024544, you need to make anchor sizes cover
25/4 * 23/4
26/4 * 13/4
I meant that i have a data set which consist of images with different resolutions ( eg:41202240, 800450, 1080120 ,300 250 etc). So can i use this dataset for training? Or TLT didn’t only unique sized images ?
For detectnet_2 and SSD network, all of the images must be resized offline to the final training size.
For faster-rcnn, you don’t need to resize the image.