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.
Related
I have a dataset of images that have two folders: test and training. I need to do object detection using OpenCV and Yolo.
Thus, I need to create my own Yolo model for the street objects.
For the training folder:
training
Example training image:
training image
For the test folder:
test
I have the classes txt file which includes id, name and classification (warning, indication and mandatory).
Example:
0 = animal crossing (warning)
1 = soft verges (warning)
2 = road narrows (warning)
Here, the numbers are the numbers (or ids) in the training folder, names, and classification.
My purpose is to create a Yolo model from these training images. I have checked some papers and articles, but in their case, they label the full image using labelimg, but in my case training images are so small and they don't need any labeling.
Thus, I'm confused about how to do this. Could you please give me some ideas?
Labeling images is a must in YOLO's that's how they deal with their loss functions. To detect objects something called (intersection over union )
More easy way to label images is by using (roboflow site ).
I would refer to this image that describes the different types of computer vision tasks.
I think what you want to do is a Classification tasks. Yolo is for Object Detection tasks, where you usually want to detect more than one object per image.
For classification tasks, it can be easier because you don't need to make separate label files. The names of the folders are the labels. Here is an example of a classification model that you can use https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
If you really want to use Yolo you will need to make label files. If you are going to do Classification of the whole image then the format of the annotation will be easy. It would be something like this.
`0 0.5 0.5 1 1' The first column is the class number: 0,1,2,3 etc. You will need to make one file for each image with the name .txt.
Does this help 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 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.
i would like to train a CNN for detection and classification of any kind of signs (mainly laboratory and safety markers) using tensorflow.
While I can gather enough training data for the classification training set, using e.g. The Bing API, I‘m struggeling to think about a solution to get enough images for the object detection training set. Since these markers are mostly not public available, I thought I could make a composite of a natrual scene image with the image of the marker itself, to get a training set. Is there any way to do that automatically?
I looked at tensorflow data augmentation class, but it seems it only provides functionality for simpler data augmentation tasks.
You can do it with OpenCV as preprocessing.
The algorithm follows:
Choose a combination of a natural scene image and a sign image randomly.
Sample random position in the natural scene image where the sign image is pasted.
Paste the sign image at the position.
Obtain the pasted image and the position as a part of training data.
Step1 and 2 is done with python standard random module or numpy.
Step3 is done with opencv-python. See overlay a smaller image on a larger image python OpenCv
.
I'm currently working on a program that can do binary image classification with machine learning. I have a list of labels and a list of images that i'm using as inputs which are then fed into the Inception V3 model.
Will inputting of the dataset this way work with the inception V3 architecture? Is it necessary to organize the images with labeled folders before feeding it into the model?
Thanks for your help!
In your example, you have all the images in memory. You can simply call model.fit(trainX, trainY) to train your model. No need to organize the images in specific folder structures.
What you are referring to, is the flow_from_directory() method of the ImageDataGenerator. This is an object that will yield images from the directories, and automatically infer the labels from the folder structure. In this case, your images should be arranged in one folder per label. Since the ImageDataGenerator is a generator, you should use it in combination with model.fit_generator().
As a third option, you can write your own custom generator that yields both images and labels. This is advised in case you have a more complex label structure than one label per images; for instance in multi-label classification, object detection or semantic segmentation, where the outputs are also images. A custom generator should also be used with model.fit_generator().