Any suggestions for a - user-friendly- rotated object detection architecture?

@all Implementing rotated object detection, especially for irregularly shaped objects like fish, can be a challenging but rewarding task. Here are some suggestions for a user-friendly approach using rotated bounding boxes and available GitHub code:

Choose a Framework: Start by selecting a deep learning framework that supports rotated object detection. TensorFlow and PyTorch are popular choices. Make sure to install the necessary libraries.

Pre-trained Models: Utilize pre-trained object detection models as a starting point. These models have been trained on large datasets and can be fine-tuned for your specific task. Some popular pre-trained models include Faster R-CNN, YOLO, and EfficientDet.

Data Preparation:Annotate your dataset with rotated bounding boxes that fit the fish accurately. Tools like LabelImg or VGG Image Annotator (VIA) can be useful. Convert your annotations into a format compatible with your chosen deep learning framework (e.g., COCO format for TensorFlow or PyTorch).

Model Fine-tuning:Fine-tune a pre-trained object detection model using your annotated dataset. Transfer learning from a pre-trained model will significantly speed up training and improve performance. GitHub repositories like TensorFlow Object Detection API or Detectron2 (for PyTorch) provide pre-built pipelines for fine-tuning object detection models.

Evaluation Metrics: Use appropriate evaluation metrics for rotated bounding box detection, such as Average Precision (AP) with an Intersection over Union (IoU) threshold suitable for your task.

Post-processing: Implement post-processing techniques to filter and refine the detected bounding boxes, especially if you have overlapping or closely packed fish.

Visualization: Create visualization scripts to display the results with rotated bounding boxes overlaid on the fish in your images.

Here are some GitHub repositories that provide code and resources for rotated object detection:

mmdetection: A popular object detection framework for PyTorch that includes support for rotated bounding boxes. You can find it on GitHub: https://github.com/open-mmlab/mmdetection

TensorFlow Object Detection API: This official TensorFlow repository includes object detection code with support for rotated bounding boxes. It's a versatile and well-documented framework: https://github.com/tensorflow/models/tree/master/research/object_detection

Detectron2: A PyTorch-based object detection library from Facebook AI Research that offers flexibility and customization. It has community-contributed code for rotated bounding box detection: https://github.com/facebookresearch/detectron2

SimpleDet: Another PyTorch-based object detection framework that supports rotated bounding boxes. It's designed to be user-friendly: https://github.com/TuSimple/simpledet

Remember that rotated bounding box detection can be computationally intensive and may require significant training data and computing resources. Fine-tuning and experimentation with hyperparameters may be necessary to achieve the best results for your specific dataset of fish piles.

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