Getting Started with 3D ConvNets in PyTorch

Welcome to the exciting world of 3D convolutional networks (ConvNets) using PyTorch! In this guide, we will explore how to set up and utilize 3D ConvNets, particularly I3D and 3D-ResNets. Whether you’re aiming to dive into action recognition or simply expand your analytical prowess, you’ve come to the right place.

What are 3D ConvNets?

3D ConvNets extend traditional 2D convolutional networks by adding an additional dimension: time. They’re particularly useful in situations where temporal information is essential, such as recognizing actions in video sequences.

Installation Guide

To get started, you need to clone the repository and install the required packages. Here’s a step-by-step breakdown of the installation process:

• Clone the repository:

git clone https://github.com/tomrunia/PyTorchConv3D.git

• Navigate into the project directory:

cd PyTorchConv3D

• Install required packages:

pip install -r requirements.txt

• Finally, set it up:

python setup.py install

System Requirements

Before proceeding with the installation, ensure you meet the following requirements:

• Python 2.7 or 3.5+ (developed with Python 3.7)
• Numpy (developed with version 1.15.0)
• PyTorch version 0.4.0
• TensorboardX (optional)
• PIL (optional)

Training Your Model

Now, let’s jump into the action! You can easily train a ResNet-34 on the UCF-101 dataset using the following command:

python train.py --dataset=ucf101 --model=resnet --video_path=/home/tomrunia/data/UCF-101/jpg --annotation_path=/home/tomrunia/data/UCF-101/ucfTrainTestlist/ucf101_01.json --batch_size=64 --num_classes=101 --momentum=0.9 --weight_decay=1e-3 --model_depth=34 --resnet_shortcut=A --spatial_size=112 --sample_duration=16 --optimizer=SGD --learning_rate=0.01

Understanding the Training Command: An Analogy

Think of training a model as preparing a dish in a kitchen. Each ingredient corresponds to a specific argument in the command:

• –dataset=ucf101: The type of cuisine you’re cooking (in this case, UCF-101 dinner).
• –model=resnet: The recipe you’re following (ResNet) to create the dish.
• –video_path: Where you source your ingredients (the path to video files).
• –annotation_path: The checklist to ensure everything is added correctly (annotations for training).
• –batch_size=64: The number of servings you want to prepare at once.
• –num_classes=101: How many types of dishes you can create (classes of actions).
• –learning_rate=0.01: The speed at which you adjust your recipe (the optimizer’s learning rate).

By combining these ingredients according to the recipe, you train the model, hoping it tastes as good as the original!

Troubleshooting

If you encounter any issues during installation or training, consider the following troubleshooting ideas:

• Ensure you’re using the correct Python version (preferably 3.7). If you’re using an older version, you might face compatibility issues.
• Check if all required libraries are installed. Missing any package from the requirements.txt will halt the installation.
• If your training isn’t progressing, revisit your data paths and ensure they’re correct.
• Experiment with your learning rate and batch size; sometimes, a small tweak can lead to significant improvements.
• Have a look at your annotations to ensure they’re correctly formatted.

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.

References

• Carreira and Zisserman – Quo Vadis, Action Recognition? (CVPR, 2017)
• Hara _et al._ – Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? (CVPR, 2018)