CNTK Image Detection FAST R-CNN not working on custom image data set? RRS feed

  • Question

  • Hi,

    I managed to run the sample grocery image data set on CNTK.

    I then tried Running Fast R-CNN on my own data, as described below (on azure datascience virtual windows machine):

    I annotated several image sets using VOTT and then tried running: 


    (py35) c:\\dsvm\\tools\\cntk\\Examples\\Image\\Detection\\utils\\annotations>python
    (py35) c:\\dsvm\\tools\\cntk\\Examples\\Image\\Detection\\FastRCNN>python
    Selected CPU as the process wide default device.
    Using base model:   AlexNet
    lr_per_sample:      [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.0001]
    Training model for 20 epochs.
    Training 56950164 parameters in 21 parameter tensors.
    Build info:
                    Built time: Sep 15 2017 07:42:54
                    Last modified date: Thu Sep 14 22:33:54 2017
                    Build type: Release
                    Build target: GPU
                    With 1bit-SGD: no
                    With ASGD: yes
                    Math lib: mkl
                    CUDA version: 9.0.0
                    CUDNN version: 6.0.21
                    Build Branch: HEAD
                    Build SHA1: 23878e5d1f73180d6564b6f907b14fe5f53513bb
                    MPI distribution: Microsoft MPI
                    MPI version: 7.0.12437.6
    PROGRESS: 0.00%
    PROGRESS: 0.00%
    PROGRESS: 0.00%
    PROGRESS: 0.00%
    Traceback (most recent call last):
      File '', line 32, in <module>
        trained_model = train_fast_rcnn(cfg)
      File 'c:\\dsvm\\tools\\cntk\\Examples\\Image\\Detection\\FastRCNN\\', line 345, in train_fast_rcnn
        data = od_minibatch_source.next_minibatch(min(cfg.MB_SIZE, cfg['DATA'].NUM_TRAIN_IMAGES - sample_count), input_map=input_map)
      File 'c:\\dsvm\\tools\\cntk\\Examples\\Image\\Detection\\FastRCNN\\..\\utils\\', line 70, in next_minibatch
        img_data, roi_data, img_dims, proposals, label_targets, bbox_targets, bbox_inside_weights = self.od_reader.get_next_input()
      File 'c:\\dsvm\\tools\\cntk\\Examples\\Image\\Detection\\FastRCNN\\..\\utils\\', line 63, in get_next_input
        img_data, img_dims = self._load_resize_and_pad_image(index)
      File 'c:\\dsvm\\tools\\cntk\\Examples\\Image\\Detection\\FastRCNN\\..\\utils\\', line 215, in _load_resize_and_pad_image
        self._prepare_annotations_proposals_and_stats(index, img)
      File 'c:\\dsvm\\tools\\cntk\\Examples\\Image\\Detection\\FastRCNN\\..\\utils\\', line 181, in _prepare_annotations_proposals_and_stats
        proposals = self._proposal_provider.get_proposals(index, img)
      File 'c:\\dsvm\\tools\\cntk\\Examples\\Image\\Detection\\FastRCNN\\..\\utils\\', line 277, in get_proposals
        return compute_proposals(img, num_proposals, self._proposal_cfg)
      File 'c:\\dsvm\\tools\\cntk\\Examples\\Image\\Detection\\FastRCNN\\..\\utils\\', line 106, in compute_proposals
        filtered_rects = filterRois(rects, img_w, img_h, roi_min_area, roi_max_area, roi_min_side, roi_max_side, roi_max_aspect_ratio)
      File 'c:\\dsvm\\tools\\cntk\\Examples\\Image\\Detection\\FastRCNN\\..\\utils\\', line 72, in filterRois
        assert(len(filteredRects) > 0)
    AssertionErrortx again


    To rule out stuff I made a custom image set, with the same amount of images as the Grocery dataset and even placed it in the grocery dataset folder.

    I attached my sample dataset annotated using VOTT.

    I can send my custom annotated custom image dataset by DM/email.

    Maybe someone can see what I'm doing wrong?

    thanks in advance,

    • Edited by Rudgr Wednesday, January 24, 2018 3:32 PM
    Wednesday, January 24, 2018 3:29 PM


All replies