---
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 mAP@0.5
        value: 0.691
        name: mAP@0.5(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).

![](images/annotated_1688033955437_.jpg)

**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```.