How to build a multiobject detection model using fastai?
I want to build a model that creates bbox around the objects that I want to detect and is able to properly identify them.
Hello,
Building a multi-object detection model using fastai involves several steps. Here’s a high-level overview to get you started: Official Login Link
- Set Up Your Environment
Ensure you have the necessary libraries installed:
Python
!pip install fastai
2. Prepare Your Data
You need a dataset with images and corresponding bounding box annotations. A common format is COCO or Pascal VOC. For example, you can use the Pascal VOC dataset:
Python
from fastai.vision.all import *
path = untar_data(URLs.PASCAL_2007)3. Load and Annotate Data
Use fastai’s get_annotations function to load your images and their bounding boxes:
Python
imgs, lbl_bbox = get_annotations(path/‘train.json’)
img2bbox = dict(zip(imgs, lbl_bbox))
Best Regards
esther598
Well, could you tell me the part on how to train the model on the data.
Thanks for your response 
I’ve been following this Tutorial and noticed that it concludes with a caveat: " show_results and predict both do not currently work. I’d recommend utilizing the IceVision library for your Object Detection needs."
However, upon exploring IceVision, I found the documentation to be lacking and the codebase hasn’t been updated in two years, which makes me hesitant to adopt it for my needs.