aicook Computer Vision Project

Background Information

This dataset was curated and annotated by - Karel Cornelis.

The original dataset (v1) is composed of 516 images of various ingredients inside a fridge. The project was created as part of a groupwork for a postgraduate applied AI at Erasmus Brussels - we made an object detection model to identify ingredients in a fridge.

From the recipe dataset we used (which is a subset of the recipe1M dataset) we distilled the top50 ingredients and used 30 of those to randomly fill our fridge.

Read this blog post to learn more about the model production process: How I Used Computer Vision to Make Sense of My Fridge

Watch this video to see the model in action: AICook

The dataset is available under the MIT License.

Getting Started

You can download this dataset for use within your own project, fork it into a workspace on Roboflow to create your own model, or test one of the trained versions within the app.

Dataset Versions

Version 1 (v1) - 516 images (original-images)

• Preprocessing: Auto-Orient
• Augmentations: No augmentations applied
• Training Metrics: This version of the dataset was not trained

Version 2 (v2) - 3,050 images (aicook-augmented-trainFromCOCO)

• Preprocessing: Auto-Orient, Resize (Stretch to 416x416)
• Augmentations:
  • Outputs per training example: 8 Rotation: Between -3° and +3° Exposure: Between -20% and +20% Blur: Up to 3px Noise: Up to 5% of pixels Cutout: 12 boxes with 10% size each
• Training Metrics: Trained from the COCO Checkpoint in Public Models ("transfer learning") on Roboflow
  • mAP = 97.6%, precision = 86.9%, recall = 98.5%

Version 3 (v3) - 3,050 images (aicook-augmented-trainFromScratch)

• Preprocessing: Auto-Orient, Resize (Stretch to 416x416)
• Augmentations:
  • Outputs per training example: 8 Rotation: Between -3° and +3° Exposure: Between -20% and +20% Blur: Up to 3px Noise: Up to 5% of pixels Cutout: 12 boxes with 10% size each
• Training Metrics: Trained from "scratch" (no transfer learning employed) on Roboflow
  • mAP = 97.9%, precision = 79.6%, recall = 98.6%

Version 4 (v4) - 3,050 images images (aicook-augmented)

• Preprocessing: Auto-Orient, Resize (Stretch to 416x416)
• Augmentations:
  • Outputs per training example: 8 Rotation: Between -3° and +3° Exposure: Between -20% and +20% Blur: Up to 3px Noise: Up to 5% of pixels Cutout: 12 boxes with 10% size each
• Training Metrics: This version of the dataset was not trained

Karel Cornelis - LinkedIn