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@@ -46,6 +46,21 @@ The predicted numerical classes correspond to the following cherry types:
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  {0: "dark_brown_cherry", 1: "green_cherry", 2: "red_cherry", 3: "yellow_cherry"}
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  ```
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  ### Demonstration
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  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```.
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  {0: "dark_brown_cherry", 1: "green_cherry", 2: "red_cherry", 3: "yellow_cherry"}
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  ```
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+ ### Repository structure
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+
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+ ```
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+ .
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+ β”œβ”€β”€ images
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+ β”‚Β Β  β”œβ”€β”€ 1688033955437.jpg # image for test
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+ β”‚Β Β  └── annotated_1688033955437_.jpg
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+ β”œβ”€β”€ model_v3_202402021.pt # fine tuning of Yolo v8
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+ β”œβ”€β”€ README.md
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+ └── scripts
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+ β”œβ”€β”€ render_results.py # helper function to annotate predictions
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+ β”œβ”€β”€ requirements.txt # pip requirements
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+ └── test_script.py # test script
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+ ```
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+
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  ### Demonstration
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  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```.
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