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.
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
π 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.
π Model Details
- Architecture: YOLOv8 (Ultralytics)
- Task: Object Detection
- Framework: PyTorch / Ultralytics
- Base Model: YOLOv8n
- Classes: 12 food categories
- Image Size: 640x640
- Training Epochs: 50
π± 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
π 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()
π Example Output
The model detects food items and draws bounding boxes around them.
Example inference result:
Results Results Results
π 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
β οΈ Limitations
This model has several known limitations:
β Dataset is imbalanced across classes some classes have very few samples (e.g., Capcay)
β Detection may be inaccurate for: visually similar foods (e.g., Tahu vs Tempe) small objects in complex scenes
β Model does NOT perform classification beyond bounding box detection
β No calorie estimation or nutritional inference
β Performance may degrade in: low light conditions heavy occlusion non-food or abstract images
π‘ 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
π License
This project is released under the MIT License.
Feel free to use, modify, and improve.
π 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
Sources
1 sourceVerified Sep 27
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- license: mit base_model: - Ultralytics/YOLOv8 tags: - indonesian - food - detection - yolov8 - computer-vision - object-detection --- # π 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. --- ## π Model Details - **Architecture:** YOLOv8 (Ultralytics) - **Task:** Object Detection - **Framework:** PyTorch / Ultralytics - **Base Model:** YOLOv8n - **Classes:** 12 food categories - **Image Size:** 640x640 - **Training Epochs:** 50 --- ## π± 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 --- ## π 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() ``` ## π Example Output The model detects food items and draws bounding boxes around them. Example inference result:    ## π 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 ## β οΈ Limitations This model has several known limitations: β Dataset is imbalanced across classes some classes have very few samples (e.g., Capcay) β Detection may be inaccurate for: visually similar foods (e.g., Tahu vs Tempe) small objects in complex scenes β Model does NOT perform classification beyond bounding box detection β No calorie estimation or nutritional inference β Performance may degrade in: low light conditions heavy occlusion non-food or abstract images ## π‘ 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
