Science Score: 54.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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✓Academic publication links
Links to: arxiv.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (10.7%) to scientific vocabulary
Repository
sea cucumber
Basic Info
- Host: GitHub
- Owner: OUCVisionGroup
- Language: Python
- Default Branch: master
- Size: 3.26 MB
Statistics
- Stars: 7
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
TISC-Net: Towards the in-situ Trunk Identification and Length Measurement of Sea Cucumbers via Bezier Curve Modelling
- 🔭 I’m currently working on something cool.
Examples of trunk identification with the proposed TISC-Net. Outcomes of TISC-Net-m are presented. Each image is followed by the enlarged slice of the target, with B´ezier curve control points marked by solid circles. The images have been slightly resized for alignment.
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Installation
You need to install Ultralytics and configure your environment according to YOLOv8's official procedures.
Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.
bash
pip install ultralytics
Models
Name | Params(M) | download DUO| download ISTI --- |:---:|:---|:--- TISC-Net-m | 26.4 | DUO_model| ISTI_model TISC-Net-xP6 | 99.3 | DUO_model| ISTI_model
Dataset preparation
- You need to prepare datasets for following training and testing activities. Datasets
- The SC-ISTI dataset comprises 462 RGB images captured by an underwater robot in real habitat of sea cucumbers, with 670 sea cucumbers.
- The SC-DUO dataset consists of 1, 023 images containing 1, 856 sea cucumbers, which is created by selecting images containing sea cucumbers from DUO dataset. ## Quick Start
Inference with Pre-trained Models
- Pick a model and its config file, for example,
yolov8-pose-FEE.yaml. - Download the model m_DUO.pt
- Run the "test.py".
model = YOLO('/data/m_DUO.pt') img_path = 'fig/2258.jpg' - You need to replace the address of the model and images.
Train Your Own Models
To train a model with "train.py", first prepare the custom dataset and set up the corresponding dataset yaml file sc_DUO-pose.yaml, then run "train.py":
``` from ultralytics import YOLO
Load a model
model = YOLO('yolov8m-pose-FEE.yaml') # build a new model from YAML
Train the model
model.train(data='sc_DUO-pose.yaml', epochs=100, imgsz=640, device = 3, batch = 32 ) metrics = model.val() # evaluate model performance on the validation set metrics.box.map # map50-95 metrics.box.map50 # map50 metrics.box.map75 # map75 metrics.box.maps # a list con
```
You are my 
visitor,Thank You!
Owner
- Name: OUCVisionGroup
- Login: OUCVisionGroup
- Kind: organization
- Repositories: 1
- Profile: https://github.com/OUCVisionGroup
Citation (CITATION.cff)
cff-version: 1.2.0
preferred-citation:
type: software
message: If you use this software, please cite it as below.
authors:
- family-names: Jocher
given-names: Glenn
orcid: "https://orcid.org/0000-0001-5950-6979"
- family-names: Chaurasia
given-names: Ayush
orcid: "https://orcid.org/0000-0002-7603-6750"
- family-names: Qiu
given-names: Jing
orcid: "https://orcid.org/0000-0003-3783-7069"
title: "YOLO by Ultralytics"
version: 8.0.0
# doi: 10.5281/zenodo.3908559 # TODO
date-released: 2023-1-10
license: AGPL-3.0
url: "https://github.com/ultralytics/ultralytics"
GitHub Events
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- Watch event: 6
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Last Year
- Watch event: 6
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Dependencies
- pytorch/pytorch 2.0.0-cuda11.7-cudnn8-runtime build
- Pillow >=7.1.2
- PyYAML >=5.3.1
- matplotlib >=3.2.2
- opencv-python >=4.6.0
- pandas >=1.1.4
- psutil *
- requests >=2.23.0
- scipy >=1.4.1
- seaborn >=0.11.0
- torch >=1.7.0
- torchvision >=0.8.1
- tqdm >=4.64.0