Upload README.md

README.md

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+# Bottle Detection with Ultralytics YOLO
+
+This project provides scripts and instructions to train and run inference on a bottle (or bottle cap) detection dataset using Ultralytics YOLO (v8+).
+
+## Environment Setup
+
+1. **Install dependencies** (recommended: use a virtual environment):
+	 ```bash
+	 python3 -m venv .venv
+	 source .venv/bin/activate
+	 pip install ultralytics
+	 ```
+
+2. **Prepare your dataset**
+	 - Organize your dataset as:
+		 ```
+		 dataset/
+			 images/
+				 train/
+				 val/
+			 labels/
+				 train/
+				 val/
+			 data.yaml
+		 ```
+	 - The `data.yaml` should specify absolute paths for `train` and `val` images.
+
+## Training a Model
+
+Edit and run `train.py` to train a model from scratch or fine-tune a pretrained model:
+
+```
+from ultralytics import YOLO
+
+model = YOLO('yolov8n.pt')  # or your custom/pretrained .pt file
+
+model.train(
+		data='/absolute/path/to/data.yaml',
+		epochs=50,
+		imgsz=640,
+		batch=16,
+		project='runs/train',
+		name='experiment_name',
+)
+```
+
+**Tips:**
+- You can adjust `epochs`, `imgsz`, `batch`, and other parameters as needed.
+- For small objects, consider increasing `imgsz` (e.g., 1024).
+- Use a GPU for best performance.
+
+## Inference with a Trained Model
+
+Edit and run `infer.py` to run inference on images:
+
+```
+from ultralytics import YOLO
+
+model = YOLO('/absolute/path/to/weights/best.pt')
+
+results = model.predict(
+		source='/path/to/image/or/folder',
+		save=True,           # Save images with boxes
+		conf=0.15,           # Confidence threshold
+		classes=[0, 1]       # (Optional) restrict to certain classes
+)
+```
+
+Output images with bounding boxes will be saved in a `runs/predict` directory.
+
+## Using Pretrained Models
+
+You can use any YOLOv8 pretrained weights (e.g., `yolov8n.pt`, `yolov8s.pt`) as the starting point for training or inference by changing the model path in the scripts.
+
+## References
+
+- [Ultralytics YOLO Docs](https://docs.ultralytics.com/)
+- [YOLOv8 GitHub](https://github.com/ultralytics/ultralytics)