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Yolo8 Indonesian Food Detection v1

This model is a YOLOv8-based object detection model trained to detect various Indonesian and international food items from images.

KindOtherBase modelUltralytics/YOLOv8VersionRevision 2d958b9

Model source

Kind
Other
File size
5.95 MB
Base model
Ultralytics/YOLOv8
Version
vRevision 2d958b9
Updated
Sep 27, 2026
Downloads
7
Tasks
Object detection
Source
Hugging Face

Source description

🍜 YOLOv8 Indonesian Food Detection v1

This model is a YOLOv8-based object detection model trained to detect various Indonesian and international food items from images.

It is trained using a COCO-format dataset converted into YOLO format and fine-tuned using Ultralytics YOLOv8.


πŸ“Œ Model Details

  • Architecture: YOLOv8 (Ultralytics)
  • Task: Object Detection
  • Framework: PyTorch / Ultralytics
  • Base Model: YOLOv8n
  • Classes: 12 food categories
  • Image Size: 640x640
  • Training Epochs: 50

🍱 Classes

The model can detect the following food categories:

  • 0: Ayam Goreng
  • 1: Bakso
  • 2: Capcay
  • 3: Mie Goreng
  • 4: Nasi Goreng
  • 5: Pempek
  • 6: Rendang Sapi
  • 7: Sate
  • 8: Tahu Goreng
  • 9: Tempe Goreng
  • 10: Terong Balado
  • 11: Tumis Kangkung

πŸš€ Usage

Install dependencies

pip install ultralytics
from ultralytics import YOLO

model = YOLO("https://huggingface.co/wuriyanto/yolo8-indonesian-food-detection-v1/resolve/main/model.pt")

results = model("food.jpg", conf=0.25)

import matplotlib.pyplot as plt
plt.imshow(results[0].plot()[:, :, ::-1])
plt.axis("off")
plt.show()

πŸ“Š Example Output

The model detects food items and draws bounding boxes around them.

Example inference result:

Results Results Results

πŸ“š Dataset

  • Source: COCO-format Indonesian Food Dataset (multi-source)
  • Converted to YOLO format for training
  • Includes mixed Indonesian + international food categories
  • Split: Train / Validation / Test

⚠️ Limitations

This model has several known limitations:

❌ Dataset is imbalanced across classes some classes have very few samples (e.g., Capcay)

❌ Detection may be inaccurate for: visually similar foods (e.g., Tahu vs Tempe) small objects in complex scenes

❌ Model does NOT perform classification beyond bounding box detection

❌ No calorie estimation or nutritional inference

❌ Performance may degrade in: low light conditions heavy occlusion non-food or abstract images

πŸ’‘ Future Improvements (v2 roadmap)

  • Dataset balancing (increase low-sample classes like Capcay)
  • Remove non-food or irrelevant classes
  • Improve annotation consistency
  • Add segmentation version (YOLOv8-seg)
  • Integrate food recognition + calorie estimation pipeline

πŸ“œ License

This project is released under the MIT License.

Feel free to use, modify, and improve.

πŸ“– Citation

If you use this model, please cite:

@misc{wuriyanto2026yolo8indonesianfood,
  title  = {YOLOv8 Indonesian Food Detection v1},
  author = {Wuriyanto},
  year   = {2026},
  url    = {https://huggingface.co/wuriyanto/yolo8-indonesian-food-detection-v1}
}

Sources

1 source
Hugging FacePrimaryVersion 2d958b91b95e40316659b81280d10ff65d8b5ba9
huggingface.co/wuriyanto/yolo8-indonesian-food-detection-v1/tree/2d958b91b95e40316659b81280d10ff65d8b5ba9

Verified Sep 27

Model artifacts

1 artifact

model.pt

pt Β· 5.95 MB Β· SHA-256 f9f8849dfd09…28b0 Β· Hugging Face

Download

Source excerpts

2 excerpts

--- license: mit base_model: - Ultralytics/YOLOv8 tags: - indonesian - food - detection - yolov8 - computer-vision - object-detection --- # 🍜 YOLOv8 Indonesian Food Detection v1 This model is a **YOLOv8-based object detection model** trained to detect various **Indonesian and international food items** from images. It is trained using a COCO-format dataset converted into YOLO format and fine-tuned using Ultralytics YOLOv8. --- ## πŸ“Œ Model Details - **Architecture:** YOLOv8 (Ultralytics) - **Task:** Object Detection - **Framework:** PyTorch / Ultralytics - **Base Model:** YOLOv8n - **Classes:** 12 food categories - **Image Size:** 640x640 - **Training Epochs:** 50 --- ## 🍱 Classes The model can detect the following food categories: - 0: Ayam Goreng - 1: Bakso - 2: Capcay - 3: Mie Goreng - 4: Nasi Goreng - 5: Pempek - 6: Rendang Sapi - 7: Sate - 8: Tahu Goreng - 9: Tempe Goreng - 10: Terong Balado - 11: Tumis Kangkung --- ## πŸš€ Usage ### Install dependencies ```bash pip install ultralytics ``` ```python from ultralytics import YOLO model = YOLO("https://huggingface.co/wuriyanto/yolo8-indonesian-food-detection-v1/resolve/main/model.pt") results = model("food.jpg", conf=0.25) import matplotlib.pyplot as plt plt.imshow(results[0].plot()[:, :, ::-1]) plt.axis("off") plt.show() ``` ## πŸ“Š Example Output The model detects food items and draws bounding boxes around them. Example inference result: ![Results](assets/result1.png) ![Results](assets/result2.png) ![Results](assets/result3.png) ## πŸ“š Dataset - Source: COCO-format Indonesian Food Dataset (multi-source) - Converted to YOLO format for training - Includes mixed Indonesian + international food categories - Split: Train / Validation / Test ## ⚠️ Limitations This model has several known limitations: ❌ Dataset is imbalanced across classes some classes have very few samples (e.g., Capcay) ❌ Detection may be inaccurate for: visually similar foods (e.g., Tahu vs Tempe) small objects in complex scenes ❌ Model does NOT perform classification beyond bounding box detection ❌ No calorie estimation or nutritional inference ❌ Performance may degrade in: low light conditions heavy occlusion non-food or abstract images ## πŸ’‘ Future Improvements (v2 roadmap) - Dataset balancing (increase low-sample classes like Capcay) - Remove non-food or irrelevant classes - Improve annotation consistency -...

Source context: 7 downloads Β· 0 likes Β· Pipeline object-detection Β· Repo wuriyanto/yolo8-indonesian-food-detection-v1