The Kesci underwater object detection challenge has taken the world by storm, operating at a remarkable mean Average Precision (mAP) of 48.7. This article will guide you through implementing the state-of-the-art Cascade R-CNN detection algorithm using various components, so let’s dive in!

Understanding the Setup

Before we start building the underwater object detection model, let’s break down some significant aspects required for setting up:

• Operating System: Ubuntu 16.10
• GPU: NVIDIA 2080Ti * 4
• Python Version: 3.7
• NVIDIA Settings:
• CUDA: 10.0.130
• CUDNN: 7.5.1
• NVIDIA Driver Version: 430.14
• Deep Learning Libraries: PyTorch 1.1.0

Getting Started

Here’s a step-by-step guide to get you from the code to deployment:

Step 1: Create the Environment

conda create -n underwater python=3.7 -y
conda activate underwater

Step 2: Install Required Packages

conda install pytorch=1.1.0 torchvision=0.3.0 cudatoolkit=10.0 -c pytorch
pip install cython
pip --no-cache-dir install -r requirements.txt

Step 3: Set Up CUDA

python setup.py develop

Step 4: Training the Model

To begin training the model, run the following commands based on the architecture you wish to implement:

• For ResNet50:

chmod +x tools/dist_train.sh
./tools/dist_train.sh configs/underwater/cascade_rcnn_r50_fpn_1x.py 4

• For SE50:

chmod +x tools/dist_train.sh
./tools/dist_train.sh configs/underwater/cascade_rcnn_se50_12ep.py 4

• For ResNext101:

chmod +x tools/dist_train.sh
./tools/dist_train.sh configs/underwater/cascade_rcnn_x101_64x4d_fpn_1x.py 4

Post-Processing and Evaluation

Once you have trained your model successfully, you will want to evaluate its performance:

python tools/post_process/json2submit.py --test_json cas_r50.bbox.json --submit_file cas_r50.csv
python tools/post_process/json2submit.py --test_json cas_se50.bbox.json --submit_file cas_se50.csv
python tools/post_process/json2submit.py --test_json cas_x101.bbox.json --submit_file cas_x101.csv

Troubleshooting Tips

If you encounter issues, here are a few troubleshooting ideas:

• Ensure all package versions match the requirements listed in your setup.
• If CUDA fails, check its compatibility with your GPU drivers.
• Verify your Python version as incorrect versions can lead to unexpected errors.
• Examine file paths and permissions, ensuring you have access to the necessary datasets and script files.

For additional questions related to AI development, please check for solutions or reach out to experts. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.