Why do we need to use Labelimg tool for object detection?
After labeling the bunch of training images using labelimg tool which will give CSV file How that CSV file works with TensorFlow object detection API and Keras?
Can we detect image localization without an image annotation tool? like auto annotation.
Topic object-detection keras tensorflow image-classification computer-vision
Category Data Science
You do not have labels in your dataset images, you cannot train a model for object detection/segmentation, etc. An image annotation tool is used for you to create labels for a dataset that enables you to train models on.
To answer your second question, after you get the CSV file, the way I recommend you proceed is to convert that CSV file in the coco format. Most open-source projects on git with object detection or localization support the coco format, so you can directly go ahead and start training with minimal code changes
As per my understanding, ML works on the concept of the type of Input+Output you feed during training, will expect(Input) and predict the same output.
So labeling is must for Supervised learning.
For Image localization use case, you have to trained your model with similar type of data i.e. label should have full annotation/details with respect to dimensions(x,y,height,width) and object class. Only then you can expect the Object localization(bounding box) during prediction.
Few of my posts possibly be helpful for you:
How YOLO training and prediction works for an object fall in multiple grid?
YOLO: How many bounding boxes?
Please go through these videos for more details. Object Detetction and Localization
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