This repository contains the implementation of the real time text detection project for CS7643. The repository is adapted from the YOLOv7 with additional tools and tuning for training on the COCO-Text V2.0 dataset.

To train the real time text detection project, you will need the following:

1. Download the COCO-Text V2.0 dataset from here. You will need both the annotations and the MS COCO 2014 images.
2. Run the convert-coco-to-yolo.ipynb to convert the COCO-Text V2.0 dataset to YOLO ready format for training.
3. Install the required packages from the requirements.txt file
4. In the python environment, we can now train the model. An example training command is: python train.py --workers 8 --device 0 --batch-size 16 --data data/coco-textbox.yaml --img 1280 --cfg cfg/training/yolov7-textbox.yaml --weights "yolov7-tiny.pt" --name yolov7-textbox-tiny-1280-rect --hyp data/hyp.scratch.textbox-tiny-1280-rect.yaml --epochs 50 --adam

To perform inference, you will need the following:

1. Install the required packages from the requirements.txt file.
2. Using the trained model weights or downloading the pretrained model weights, we can now run inference in the python environment. An example inference command is: python detect.py --weights runs/train/yolov7-textbox-tiny-1280/weights/best.pt --conf 0.25 --img-size 1280 --source coco-text/images/test/img18000.jpg --device 0

The YOLOv7-e6e model inference failed to run due to compute limitations.