frcnn | Faster R-CNN / R-FCN : bulb : C version based on Caffe | Machine Learning library

So, I am trying to train faster rcnn model for my malaria data. I cloned the repository from https://github.com/kbardool/keras-frcnn.git . I added all the image file and the script itself inside the cloned folder but whenever I try to run the train_frcnn.py script, it shows me syntax error when there is no syntax error anywhere. What could the reason be?

The Error

I use OD-API to train models. I have two question please regarding the way of processing backgrounds images and images that have same object labeled twice (or more) of different label names, and that when using faster_rcnn_resnet101 and SSD_mobilenet_v2.

1- When an image has no ground truth boxes(background image) do we generate Anchor boxes for them in case of using fRCNN (or default boxes for the SSD) even though we don't have GT boxes? Or the whole image in such a case will be a negative example?

2- When an image has two (or more) GT boxes that have same coordinates, but different label names, does this make issues when matching with Anchor boxes (or default boxes for the SSD)? like only one of the GT boxes will be matched here?

I will be glad for any help, I tried reading papers, tutorials and books but couldn't find answers or maybe I am missing something. Regarding question 2, Prof. Andrew Ng said at 6:55 of this video about Anchor Boxes in YOLO, that such cases, when we have multiply objects in the same grid cell, these cases can't be handled well. So maybe the same applies to my cases, even though I don't know what happens as a result in my cases. Also I think these files target_assigner.py and argmax_matcher.py have some clues, but also I can't really confirm.

Thank you in advance

I am trying to train MaskRCNN to detect and segment apples using the dataset from this paper,

github link to code being used

I am simply following the instructions as provided in the ReadMe file..

Here is the output on console

I have trained a Mask RCNN network using PyTorch and am trying to use the obtained weights to predict the location of apples in an image..

I am using the dataset from this paper, and here is the github link to code being used

I am simply following the instructions as provided in the ReadMe file..

Here is the command i wrote in prompt (removed personal info)

python predict_rcnn.py --data_path "my_directory\datasets\apples-minneapple\detection" --output_file "my_directory\samples\apples\predictions" --weight_file "my_directory\samples\apples\weights\model_19.pth" --mrcnn

model_19.pth is the file with all the weights generated after the 19th epoch

Error is as follows:

Loading model Traceback (most recent call last): File "predict_rcnn.py", line 122, in main(args) File "predict_rcnn.py", line 77, in main model.load_state_dict(checkpoint['model'], strict=False) KeyError: 'model'

I will paste predict_rcnn.py for convenience:

I am using Tensorflow object detection API and I have trained two separate models( FRCNN Inception V2 and SSD Mobilenet V2). In my code flow, when both of the models have been trained, I need to export inference graphs. The following is the code for the same:

I am working with TensorFlow object detection API, I have trained two different(SSD-mobilenet and FRCNN-inception-v2) models for my use case. Currently, my workflow is like this:

1. Take an input image, detect one particular object using SSD mobilenet.
2. Crop the input image with the bounding box generated from step 1 and then resize it to a fixed size(e.g. 200 X 300).
3. Feed this cropped and resized image to FRCNN-inception-V2 for detecting smaller objects inside the ROI.

Currently at the time of inferencing, when I load two separate frozen graphs and follow the steps, I am getting my desired results. But I need only a single frozen graph because of my deployment requirement. I am new to TensorFlow and wanted to combine both graphs with crop and resizing process in between them.