# Flowchart of Computer Vision **URL:** \ **Category:** Uncategorized\ **Tags:** image\ **Created:** [February 7, 2023, 4:28am UTC](https://support.prodi.gy/t/flowchart-of-computer-vision/6348 "2023-02-07T04:28:23Z")\ **Posts on this page:** 1\ **Showing post:** 5
**Author:** ![ryanwesslen](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ryanwesslen/32/2969_2.png) [@ryanwesslen](https://support.prodi.gy/u/ryanwesslen)\ **Post date:** [February 8, 2023, 12:17pm UTC](https://support.prodi.gy/t/flowchart-of-computer-vision/6348/5 "2023-02-08T12:17:40Z")
> [@kushalrsharma](#): > > This means i need to first annotate using prodigy and need to make a script to convert them to either YOLO format or COCO format for training. Or is there any scripts that helps me to do so? Check out these two posts: > [@image classification output for Yolo in 2022](https://support.prodi.gy/t/image-classification-output-for-yolo-in-2022/5156/2): > > Hi @info2000, the output annotations are still in a JSONL file, and you still need to parse it to the YOLO format afterwards. You can refer to this [blogpost](https://mlops.systems/redactionmodel/computervision/datalabelling/2021/11/29/prodigy-object-detection-training.html) that did a similar approach (although here he's converting it into the COCO format but he also showed what a typical image annotation looks like) > [@Datasets and using pre-annotated data](https://support.prodi.gy/t/datasets-and-using-pre-annotated-data/1476/26): > > I just made a demo COCO display recipe that allows me to simply visualize the dataset and review it for correctness It might be a good starting point for your own workflow If you find something that works, we would be grateful if you could post to help others like you in the future! --- _[View the full topic](https://support.prodi.gy/t/flowchart-of-computer-vision/6348)._