How Can I change the value of "maxdets" in Faster R-CNN by Pytorch?

I am implementing a faster RCNN network on pytorch. I have followed the next tutorial.

https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html

There are images in which I have more than 100 objects to classify. However, with this tutorial I can only detect a maximum of 100 objects, since the parameter "maxdets" = 100.

Is there a way to change this value to adapt it to my project?

IoU metric: bbox
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.235
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.655
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.105
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.238
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.006
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.066
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.331
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.331
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000

If only change next param, it would be the problem solved?

cocoeval.Params.setDetParams.maxDets = [1, 10, 100]

Thank you!

Solution

"There are images in which I have more than 100 objects to classify."

maxDets = 100 doesn't mean it will classify only 100 images but it refers to % AverageRecall given 100 detections per image

inshort maxDets is realted to metrics not actual no. of images classified.

for more info visit : http://cocodataset.org/#detection-eval

Tensorboard graph recall

https://github.com/matterport/Mask_RCNN/issues/663

 # Limit to max_per_image detections **over all classes**
    if number_of_detections > self.detections_per_img > 0:
        cls_scores = result.get_field("scores")
        image_thresh, _ = torch.kthvalue(
            cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
        )
        keep = cls_scores >= image_thresh.item()
        keep = torch.nonzero(keep).squeeze(1)
        result = result[keep]
    return result

according to this code snippet I found that it checks the no. of detection so model.roi_heads.detections_per_img=300 is correct for your purpose. And I haven't found much proper documentation on maxdets but i guess the above code should work.

 # non-maximum suppression, independently done per class
   keep = box_ops.batched_nms(boxes, scores, labels, self.nms_thresh)
 # keep only topk scoring predictions
   keep = keep[:self.detections_per_img]

this code snippet says that we can filter out only some top detections we want to have in our model.