I'm using Pyro to indentify custom objects in an Image. I want to evaluate the performance of the model by comparing it with the Ground truth. To generate the ground truth, I wanted to automate the process by drawing bounding box around the image(using my model) and letting the user correct the bounding box to help save time. For each bounding box I want to have a label, sub label, group the same lables and decription. What would be the best way to do this ? LabelImg dosen't give an option of sublabels and decription.
I have trained my model to draw bounding boxes around images
from detecto import core, utils, visualize
model = core.Model.load(r'..\Downloads\model_weights.pth', ['Person', 'Dog', 'Cat'])
image = utils.read_image('13.jpg')
predictions = model.predict(image)
# predictions format: (labels, boxes, scores)
labels, boxes, scores = predictions
print(labels,scores)
print(boxes)
visualize.show_labeled_image(image, boxes, labels)
Now I want to automate the manual labelling process of the images by using the same algorithm. I want to draw boundry boxes around objects and let user provide label, sub label, description and group the labels. Basically, I want to speed up the manual labelling process by using my model to pre label the objects and let users make modification to the boundry boxes and add the above metnioned details to the labels
Related
There is a tutorial on the web for drawing bounding boxes using R-CCN, where a VGG16 network is modified for this task (using transfer learning take advantage that the inner layers are trained already.).
The edit consists on:
removing the classification layer
using a regression layer instead
The training involves images for inputs and [x1,y1,x2,y2] labeled outputs, each pair being a corner of an image, i.e a description of a square box around the object we want to detect.
I have tried it, and so far didn't have luck for the coordinates predicted. So my questions are:
Is the procedure of editing the CNN to create an R-CNN that outputs the vector (also in link at the top) a correct approach for predicting a bounding box for a specific object ?
I am trying with Mobile Net because it is lighter, so assuming 1. is correct, would this also be a "logically similar" idea?
I have an image segmentation problem. First I need to find a certain animal out of an image with multiple different animals. Then I need to find a certain feature in the animal. The first network build to find the particular animal is simply a unet doing binary classification. I have a resulting dice score of 96%.
Now I would like to be able to use the mask from the first network to crop the original image around the animal, I would also need to crop the second ground truth mask related to that image (this is the ground thruth for the features). How can I retreive a bounding box from the first mask predicted to be able to crop my images further?
I am coding in python and using pytorch and torchvision. I would like to avoid keras and tensorflow, any other library is welcome.
I am trying to understand RPN network in Faster RCNN.
I understand the concept of RPN network,
Pass the input images to the pre trained CNN, and get the output as feature maps
Make fixed size of the feature maps
Extract anchors (3 different scales and ratio for every sliding window) from the fixed size feature maps.
Use two 1×1 Fully connected NN to find the background or object and the bounding box coordinates (4 values)
Calculate IOU for Anchors bounding box with Ground Truth bounding box, if IOU>0.7, then the anchor has object, otherwise, the anchor has background.
The theme for RPN is to give the region proposals which have objects.
But, I do not understand the input and the output structure.
For example, I have 50 images, each images having 5 to 6 objects, and labeling informations(coordinates of each objects).
How do I generate target values, to train PRN Network...
In all the blogs, they shows the architecture as feed the entire image to the pre trained CNN.
And, the output of RPN, the model has to tell whether the anchor has object or not, and also predict the bounding box for the object in the anchor.
For this, how to prepare the input and target/output values like we do in dog/cat or dog/cat/car classification problem.
Let me correct if I am not correct,
Is that, we have to crop all the objects in every image and do binary classification as object vs background for classifying the anchor has object or not
And, Is that, we have to give the ground truth value as target for every cropped objects from all images in the dataset, so that the RPN network trained well to predict the bounding box for the object in every anchor.
Hope, I clearly explained my doubts.
Help me to learn this concept, Thank you
After training an image detection model, how do I load the parameters of the bounding boxes for a specific operation?
Model: Darkflow Yolov2
Classes:7
For instance, if I set the threshold as 0.5, how do I utilize the resultant bounding boxes in a video to calculate the overlap. I am rather new to python and would appreciate it if someone could point me in the right direction.
I am unclear how to extract the individual class detection box and their relevant x and y data. Thank you!
I am working on an object detection model. I have annotated images whose values are stored in a data frame with columns (filename,x,y,w,h, class). I have my images inside /drive/mydrive/images/ directory. I have saved the data frame into a CSV file in the same directory. So, now I have annotations in a CSV file and images in the images/ directory.
I want to feed this CSV file as the ground truth along with the image so that when the bounding boxes are recognized by the model and it learns contents of the bounding box.
How do I feed this CSV file with the images to the model so that I can train my model to detect and later on use the same to predict bounding boxes of similar images?
I have no idea how to proceed.
I do not get an error. I just want to know how to feed the images with bounding boxes so that the network can learn those bounding boxes.
We need to feed the bounding boxes to the loss function. We need to design a custom loss function, preprocess the bounding boxes and feed it back during back propagation.