How to use the project just for object detection ?

[LaviLiu]

❓ Questions and Help

I want to just just the objection function. I transfomed my dataset to coco style. But the segmentation is [] in annotation files. The annotation is like below: "annotations": [{ "segmentation": [], "area": 5332000, "iscrowd": 0, "image_id": 200, "bbox": [311, 707, 161, 135], "category_id": 1, "id": 25064 }, { "segmentation": [], "area": 5332000, "iscrowd": 0, "image_id": 200, "bbox": [351, 631, 101, 90], "category_id": 1, "id": 25065 }, ... But when I train with one GPU. I got the following errors: 2019-01-11 22:36:47,272 maskrcnn_benchmark.trainer INFO: Start training Traceback (most recent call last): File "tools/train_net.py", line 171, in <module> main() File "tools/train_net.py", line 164, in main model = train(cfg, args.local_rank, args.distributed) File "tools/train_net.py", line 73, in train arguments, File "/home/weiliu/deepcode/maskrcnn-benchmark/maskrcnn_benchmark/engine/trainer.py", line 66, in do_train loss_dict = model(images, targets) File "/home/weiliu/anaconda3/envs/py36thmr/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "/home/weiliu/deepcode/maskrcnn-benchmark/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py", line 52, in forward x, result, detector_losses = self.roi_heads(features, proposals, targets) File "/home/weiliu/anaconda3/envs/py36thmr/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "/home/weiliu/deepcode/maskrcnn-benchmark/maskrcnn_benchmark/modeling/roi_heads/roi_heads.py", line 36, in forward x, detections, loss_mask = self.mask(mask_features, detections, targets) File "/home/weiliu/anaconda3/envs/py36thmr/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__ result = self.forward(*input, **kwargs) File "/home/weiliu/deepcode/maskrcnn-benchmark/maskrcnn_benchmark/modeling/roi_heads/mask_head/mask_head.py", line 76, in forward loss_mask = self.loss_evaluator(proposals, mask_logits, targets) File "/home/weiliu/deepcode/maskrcnn-benchmark/maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py", line 114, in __call__ labels, mask_targets = self.prepare_targets(proposals, targets) File "/home/weiliu/deepcode/maskrcnn-benchmark/maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py", line 96, in prepare_targets segmentation_masks, positive_proposals, self.discretization_size File "/home/weiliu/deepcode/maskrcnn-benchmark/maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py", line 40, in project_masks_on_boxes mask = scaled_mask.convert(mode="mask") File "/home/weiliu/deepcode/maskrcnn-benchmark/maskrcnn_benchmark/structures/segmentation_mask.py", line 129, in convert [p.numpy() for p in self.polygons], height, width File "pycocotools/_mask.pyx", line 292, in pycocotools._mask.frPyObjects IndexError: list index out of range I want to know how to use the project just for object detection ?

[fmassa]

In theory, all you need to do is to set MASK_ON to False (the default value) https://github.com/facebookresearch/maskrcnn-benchmark/blob/f25c6cff92d32d92abe8965d68401004e90c8bee/configs/e2e_mask_rcnn_R_50_C4_1x.yaml#L10

What is the config file that you are using?

[hockeybro12]

Hi,

I would like to do something like what is described in this image:

In this picture, an image is passed to the Mask R-CNN model and some object detection segmentation is done on the shape. This allows a future model to more easily identify the relevant shapes, once the segmentation map is concatenated on top of the original image.

I am trying to do this on this picture:

I run the MaskRCNN as given in the demo notebook. However, the image seems to be unchanged with both options. I don't see any segmentation maps being drawn.

Is it possible to use this library to do this task? Must I fine-tune the given model to get it to work?

Thanks in advance for your help

[fmassa]

@hockeybro12 you'll need to retrain/finetune for your particular use-case. The classes that maskrcnn-benchmark were trained for can be seen in https://github.com/facebookresearch/maskrcnn-benchmark/blob/c5ca36fc644dfc1d3dd4ad15739bf6bb4df72d72/demo/predictor.py#L16-L98

[hockeybro12]

@fmassa Hmm ok. Are you by any chance aware of any other object detection models that work for shapes?

[fmassa]

@hockeybro12 not out of the box, no