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README.md

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+# Cheerios Box Detection Project
+
+This project trains a YOLOv8 model to detect Cheerios boxes in images.
+
+## Project Overview
+
+The goal of this project is to train a model to detect Cheerios boxes in a dataset of images. The original approach was to use a 3D pose estimation model, but due to several challenges, the project was pivoted to a 2D object detection model using YOLOv8.
+
+## Challenges Faced
+
+The initial approach of using the `YOLOv5-6D-Pose` model for 3D pose estimation was met with several challenges:
+
+*   **Dependency Issues:** The project had strict dependencies on specific versions of Python and PyTorch, which were not compatible with the available hardware.
+*   **Data Formatting:** The data format required by the `YOLOv5-6D-Pose` model was not well-documented, and several attempts to format the data correctly were unsuccessful.
+*   **CUDA Incompatibility:** The version of PyTorch required by the `YOLOv5-6D-Pose` model was not compatible with the available NVIDIA drivers.
+
+## Solution
+
+To overcome these challenges, the project was pivoted to a 2D object detection model using YOLOv8. This approach has several advantages:
+
+*   **Faster Training:** YOLOv8 is a much faster model to train than the `YOLOv5-6D-Pose` model.
+*   **Less Resource-Intensive:** YOLOv8 requires fewer resources to train than the `YOLOv5-6D-Pose` model.
+*   **Simplified Data Preparation:** The data preparation for YOLOv8 is much simpler than for the `YOLOv5-6D-Pose` model.
+
+## How to Run
+
+1.  **Set up the environment:**
+    ```bash
+    uv venv .venv --python 3.9
+    source .venv/bin/activate
+    uv pip install -r requirements.txt
+    ```
+2.  **Prepare the data:**
+    ```bash
+    python3 prepare_cheerios_yolov8.py
+    ```
+3.  **Train the model:**
+    ```bash
+    yolo train data=YOLOv8/cheerios_yolov8.yaml model=yolov8n.pt epochs=100 imgsz=640
+    ```