I would appreciate it if someone could help me. I’m trying to implement mask rcnn on Pascal VOC 2012. My main concerned are the transformations and AP. I resized the boxes, images and mask in the dataset class and used transforms separately. After 15 epochs I got AP Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.65 for the boxes and 0.53 for the mask. However, the AP for small objects is not higher than 0.15. I am not really sure if I am applying the transformation correctly if I need to rotate and them scale. I run another version of the code with transforms built in the class for the mask and image and separately rescale the box. I did not use the horizontal flip and I got the results below in one epoch.
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.756
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.659 ,
The issue with small objects remained.
Also, I am using the coco metrics . Does anyone know if it is possible to get the AP per class using COCO Average Precision (AP) @[ IoU=0.50?
I 've been stuck on this, any piece of advice will be greatly appreciated.
def resize_img(self, imag, img_max_size):
“”“Resizes images and masks from PIL Images”""
h_, w_ = self.img_max_size, self.img_max_size img_resized= imag.resize((h_,w_)) # returns resized image return img_resized def resize_box(self, box, img_max_size): """resizes bounding boxes""" h_ratio = img_max_size/h w_ratio = img_max_size/w box = round(box * w_ratio) box = round(box * w_ratio) box = round(box * h_ratio) box = round(box * h_ratio) return box
transforms =