metadata
tags:
- coffee
- cherry count
- yield estimate
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- object-detection
- pytorch
library_name: ultralytics
library_version: 8.0.75
inference: false
datasets:
- rgautron/croppie_coffee
model-index:
- name: rgautron/croppie_coffee
results:
- task:
type: object-detection
dataset:
type: rgautron/croppie_coffee
name: croppie_coffee
split: val
metrics:
- type: precision
value: 0.691
name: [email protected](box)
General description
Ultralytics' Yolo V8 medium model fined tuned for coffee cherry detection using the Croppie coffee dataset.
Note: the low visibility/unsure class was not used for model fine tuning
The predicted numerical classes correspond to the following cherry types:
{0: "dark_brown_cherry", 1: "green_cherry", 2: "red_cherry", 3: "yellow_cherry"}
Repository structure
.
├── images
│ ├── 1688033955437.jpg # image for test
│ └── annotated_1688033955437_.jpg
├── model_v3_202402021.pt # fine tuning of Yolo v8
├── README.md
└── scripts
├── render_results.py # helper function to annotate predictions
├── requirements.txt # pip requirements
└── test_script.py # test script
Demonstration
Assuming you are in the scripts folder, you can run python3 test_script.py. This script saves the annotated image in ../images/annotated_1688033955437.jpg.
Make sure that the Python packages found in requirements.txt are installed. In case they are not, simply run pip3 install -r requirements.txt.