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---
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 # substitute for [email protected]
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](https://huggingface.co/datasets/rgautroncgiar/croppie_coffee_split).
**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"}
```
### 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```. |