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.
Related
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
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 have a caltech101 dataset for object detection. Can we detect multiple objects in single image using model trained on caltech101 dataset?
This dataset contains only folders (label-wise) and in each folder, some images label wise.
I have trained model on caltech101 dataset using keras and it predicts single object in image. Results are satisfactory but is it possible to detect multiple objects in single image?
As I know some how regarding this. for detecting multiple objects in single image, we should have dataset containing images and bounding boxes with name of objects in images.
Thanks in advance
The dataset can be used for detecting multiple objects but with below steps to be followed:
The dataset has to be annotated with bounding boxes on the object present in the image
After the annotations are done, you can use any of the Object detectors to do transfer learning and train on the annotated caltech 101 dataset
Note: - Without annotations, with just the caltech 101 dataset, detecting multiple objects in a single image is not possible
i used the Google TensorFlow Object detection API [https://github.com/tensorflow/models][1] to train on my own dataset using Faster RCNN inception v2 model and bty writing some of my own scripts in python 3. It works fairly well on my videos and now I want to output the predicted bounding boxes to calculate mAP. IS there any way to do this?
I have three files generated from training:
model.ckpt-6839.data-00000-of-00001
model.ckpt-6839.index
model.ckpt-6839.meta
Is the predicted boxes contained in one of these files? Or are they stored somewhere else? Or do they need to be coded separately for the coordinates to be extracted?
The files you listed are checkpoint files, you can use then to export a frozen graph and then do prediction of input images.
Once you obtained the frozen graph, you can then use this file object_detection_tutorial.ipynb to do prediction of input images.
In this file, the function run_inference_for_single_image will return a output dict for each image and it contains detection boxes in it.