CoinShift Imaging Box 📦📦

CoinShift Imaging Box: A collection of efficient and fast implementation of SOTA Object Detection and Segementation Models.

Why CoinShift Imaging?

Want to get Started with Advanced Computer Vision and NLP?
- Coinshift provides you a large variety of Computer Vision and NLP pipelines.
Want to run high-end, the latest state-of-the-art models, but you lack GPU and RAM?
- All implementation in CoinShift is done using Google Collab or Kaggle Kernels

Some key Features!!

* Run training and inference in one place.
* All the implementation of NLP tasks is done by using Hugging Face.
* Train your model over customer data very easily.

Who can use the CoinShift Imaging Box?

🚩 CoinShift Imaging Box templates are meant for those people who have intermediate knowledge of Object Detection, Segementation models, and Algorithms.

🚩 CoinShift only provides you a basic template to kick start your initial part on any Detection, Segementation, Gans, NLP project or task. You would still need to add some custom cfg and other files according to your datasets or corpus.

Important Elements

A) Inference Engine
- Original pre-trained models (from original authors and implementations) for inferencing and analyzing.
- Pretrained models on coco, voc, cityscpaes, type datasets. It- Useful to analyze which algorithm works best for you.
- Useful to generate semi-accurate annotations (coco, pascal-voc, yolo formats) on a new dataset.

Create real-world Object Detection applications

Wheat detection in field	Multi-Human Pose Detection	Ship Detection in Oceans on Satellite Images

Underwater Fish Detection	Real-Time Object Detection on Crowd	Real world Object Detection using Facebook DETR

Car Detection using YoloV5	Object Detection using RetinaNET	Human Pose Detection using HRNET

Mask Detection using CNN	Tiger Detection in wild using Yolov5