https://github.com/oki-aryawan/flower-detection-yolov5
Detect hortensia dan hervetica flower using YOLOv5
https://github.com/oki-aryawan/flower-detection-yolov5
colaboratory deep-learning detection flower-recognition pytorch yolov5
Last synced: about 2 months ago
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Detect hortensia dan hervetica flower using YOLOv5
- Host: GitHub
- URL: https://github.com/oki-aryawan/flower-detection-yolov5
- Owner: oki-aryawan
- License: gpl-3.0
- Created: 2022-04-09T04:44:40.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-04-24T06:22:26.000Z (over 3 years ago)
- Last Synced: 2025-06-02T01:15:35.411Z (4 months ago)
- Topics: colaboratory, deep-learning, detection, flower-recognition, pytorch, yolov5
- Language: Jupyter Notebook
- Homepage:
- Size: 45.4 MB
- Stars: 13
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## Flower Detection with YOLOv5
This is my first deep learning project using YOLOv5. In this project I made deep learning model to detect 2 kind of flower in my house yard. There are 2 types of flowers (hortesia and mavavicus). In total I used 104 images that I took and labelled by my self.
## Tech/Framework Used
- [PyTorch](https://pytorch.org/hub/ultralytics_yolov5/)
- [YOLOv5](https://ultralytics.com/yolov5)
- [Google Colab](https://colab.research.google.com/)
- [RoboFlow](https://roboflow.com/)
- [LabelImg](https://tzutalin.github.io/labelImg/)
## Installation
### To train YOLOv5 model using my dataset
- Donwload my anotated images
- Create dataset using Roboflow
- Complete tutorial
[How to Train YOLO v5 on a Custom Dataset](https://www.youtube.com/watch?v=MdF6x6ZmLAY)
### To run my model
- Clone the YOLOv5 repository by [Ultralitics](https://ultralytics.com/)
```
git clone git@github.com:ultralytics/yolov5.git
```
- Open the yolov5 directory using IDE or code editor and setup the Python environment (I used Python 3.8 and Pycharm Professional Edition as my Python IDE)
- Get ready with all of the Python librabry that required
```
pip install -r requirements.txt
```
- Download my weights and yaml file from dataset directory
- Put those files in the yolov5 directory in your local computer
- Run the `detect.py` file using my custom weights
```bash
python detect.py --weights flower_07042022.pt --source 0 # webcam
img.jpg # image
vid.mp4 # video
path/ # directory
path/*.jpg # glob
```
- Result file will saved at `runs/detect` in yolov5 directory