https://github.com/hardikdava/eyeq
https://github.com/hardikdava/eyeq
Last synced: over 1 year ago
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- Host: GitHub
- URL: https://github.com/hardikdava/eyeq
- Owner: hardikdava
- License: gpl-3.0
- Created: 2023-04-25T15:19:24.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-06-19T07:44:43.000Z (about 3 years ago)
- Last Synced: 2025-02-08T15:13:09.044Z (over 1 year ago)
- Language: Python
- Size: 300 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# EyeQ
EyeQ is a minimal computer vision inference pakackge. Currently it supports following detectors in onnx runtime. It works with minimal dependencies. It is designed in such a manner to run on edge devices also.
--------------------
### Object Detection:
| Detector | onnx |
|--|--|
| [yolov5](https://github.com/ultralytics/yolov5) | ✅|
| [yolov6](https://github.com/meituan/YOLOv6) | ✅ |
| [yolov7](https://github.com/WongKinYiu/yolov7) | ✅ |
| [yolov8](https://github.com/ultralytics/ultralytics) | ✅ |
| [yolov5u](https://github.com/ultralytics/ultralytics) | ✅ |
| [yoloX](https://github.com/Megvii-BaseDetection/YOLOX) | ✅ |
| [Damo-yolo](https://github.com/tinyvision/DAMO-YOLO) | ✅ |
--------------------
### Instance Segmentation:
| Detector Name | onnx |
|--|--|
| [yolov5](https://github.com/ultralytics/yolov5) | ✅ |
| [yolov7](https://github.com/WongKinYiu/yolov7) | #TODO |
| [yolov8](https://github.com/ultralytics/ultralytics) | ✅ |
--------------------
### Multi Object Tracker:
| Tracker Name | Integration |
|--|--|
| [SORT](https://github.com/ultralytics/yolov5) | ✅ |
| [ByteTrack](https://github.com/WongKinYiu/yolov7) | ✅ |
| [OcSort](https://github.com/ultralytics/ultralytics) | ✅ |
| [Norfair](https://github.com/ultralytics/ultralytics) | - |
--------------------
### Installation:
Installation can be done via pip using following argument
```
pip3 install git+https://github.com/hardikdava/EyeQ.git
```
--------------------
#### TODO:
- Docker support
- RestAPI server ✅
- Multi object trackers ✅
- Instance segmentation ✅
- Yolo Dataset loading ✅
- COCO dataset loading
- Object detection evaluation ✅
- Multi object tracker evaluation ✅
- Automatic annotation support using clip, grounding dino and sam
- Introduce SAHI technique
#### Available APIs:
- Object Detection Inference using ONNX runtime
- Object Detction Evaluation API
- Model serving using RESTAPI using FastAPI based server
- Multi object Tracking for bounding boxes
- Multi object Tracking
- Instance segmentation support
- Data loading for yolo
Note: models are trained using [notebooks](https://github.com/roboflow/notebooks) prepared by roboflow but models are not included with codebase.
### References: