TomatoACLv1

Model Overview

TomatoACLv1 is an object detection model trained from scratch using the YOLO11n architecture for tomato detection in images.

This repository includes the trained model weights in both PyTorch (.pt) and ONNX (.onnx) formats, along with training configuration and result files.

Dataset

The model was trained using the following Kaggle dataset:

Tomato Detection
https://www.kaggle.com/datasets/andrewmvd/tomato-detection

Training Details

• Architecture: YOLO11n
• Training strategy: trained from scratch
• Task: object detection
• Target object: tomato

Repository Contents

• tomatoACLv1.pt — trained PyTorch model
• tomatoACLv1.onnx — exported ONNX model
• args.yaml — training arguments
• data.yaml — dataset configuration
• metrics.json — training metrics
• results.png — training summary image

Training Results

Below is the training summary image generated during training:

Intended Use

This model is intended for tomato detection in images and can be used for inference in environments compatible with:

• PyTorch
• ONNX Runtime
• other ONNX-compatible deployment frameworks

Limitations

Performance may vary depending on image quality, lighting conditions, occlusions, and differences between real-world data and the original training dataset distribution.