ego-yolo
We retraine the YOLO-series detection framework on the ego-object dataset in order to obtain a more complete egocentric perspective visual tool chain.
Science Score: 44.0%
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Low similarity (7.7%) to scientific vocabulary
Repository
We retraine the YOLO-series detection framework on the ego-object dataset in order to obtain a more complete egocentric perspective visual tool chain.
Basic Info
- Host: GitHub
- Owner: Songluchuan
- Language: Python
- Default Branch: main
- Size: 7.63 MB
Statistics
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
EGO-YOLO
We retrain the YOLO-series detection framework on the ego-object dataset in order to obtain a more complete egocentric perspective visual tool chain. The backbone for the detecor is from YOLOv5. The ego-object dataset is from the : https://ai.meta.com/datasets/egoobjects-downloads/. In this work, we do not set the object classification branch in YOLO, only the foreground (object) and background were classified. 1. We freeze the Classify Decoder and set the classification-head into a binary class structure – front ground and back ground. 2. We involve the COCO pretrained backbone and finetune on the Ego-Object Datasets 3. Reset all the data into a COCO format from detron2 format.
How to use it
Download the pretrained YOLO: The pretrained model is putted in: https://drive.google.com/drive/folders/1j6z27hA8vNAoCB8aZcYrNG2JDFEJrlu?usp=drivelink , please download the pretrained model (last.pt or best.pt).
Install the package:
pip install -r requirements.txtRun with:
python detect.py --weights best.pt --source $Your Image$
Some Experimental results
Here is the mAP-50 results without pretrained YOLOv5 and pretrained YOLOv5:
We show the val-set comparison results in below:
The pretrained results:
The origin YOLO results:
Then, we show the real-world (real headset videos) comparison results in below:
The pretrained results:
The origin YOLO results:
Owner
- Name: YYN
- Login: Songluchuan
- Kind: user
- Repositories: 1
- Profile: https://github.com/Songluchuan
Citation (CITATION.cff)
cff-version: 1.2.0
preferred-citation:
type: software
message: If you use YOLOv5, please cite it as below.
authors:
- family-names: Jocher
given-names: Glenn
orcid: "https://orcid.org/0000-0001-5950-6979"
title: "YOLOv5 by Ultralytics"
version: 7.0
doi: 10.5281/zenodo.3908559
date-released: 2020-5-29
license: AGPL-3.0
url: "https://github.com/ultralytics/yolov5"