AitorConS/tomatoACLv1
By AitorConS · ↓ 0 · ♥ 0 · Updated 2026-03-22
What's in this repo
This page lists the files in AitorConS/tomatoACLv1, including tomatoACLv1.onnx.
- 1 ONNX models (.onnx files)
- 7 files, including 2 .yaml, 1 .json, 1 .png, 1 .onnx, 1 .pt
- Last updated 2026-03-22
ONNX files
| File | Size | Download |
|---|---|---|
| tomatoACLv1.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
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 modeltomatoACLv1.onnx— exported ONNX modelargs.yaml— training argumentsdata.yaml— dataset configurationmetrics.json— training metricsresults.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.
Files (7)
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