Region Based Convolutional Neural Networks (RCNN) in Python

• This repository builds an end-to-end multi-class, multi-object image detector using RCNN which is a popular algorithm for object detection.

• Paper: Rich feature hierarchies for accurate object detection and semantic segmentation

Requirements

• Python 3
• Pytorch
• Pillow
• Matplotlib
• BeautifulSoup
• tqdm

Dataset:

• The dataset is from Kaggle: https://www.kaggle.com/datasets/andrewmvd/fruit-detection.
• The data is a set of fruit images. There are 4 types of fruits (Banana, Snake fruit, Dragon fruit, Pineapple), each image includes 3-4 objects.

RCNN Algorithm Pipeline

• The workflow is follow strickly by the pipeline below:

• Specifically, in my repository, RCNN algorithm is applied as below:

  • Step 1: The Selective Search algorithm is applied on each image to find the proposed ROIs (Region of Interest). After that, these regions are divided into 5 classes ('nothing', 'pineapple', 'snake fruit', 'dragon fruit', 'banana'). The coordinates of bounding box and label of each region are saved.
  • Step 2: A fine-tuned EfficientNet model is used to train for image classification problem on above dataset.
  • Step 3: At the beginning of inference phase, the Selective Search is applied to find the proposed boxes on infer image.
  • Step 4: Next, fine-tuned model trained above is used to predict class of each box.
  • Step 5: Apply Non-Maximum Suppression (NMS) algorithm to remove redundant boxes.
  • Step 6: Return final object detection results.

• A sample result is described below:

References

• RCNN on Raccoon dataset