Welcome to your guide on utilizing CrossViT: the Cross-Attention Multi-Scale Vision Transformer designed for image classification tasks. Here, we’ll walk you through the installation, data preparation, training, and evaluation phases. Ready? Let’s get started!

Installation

Before diving into the world of CrossViT, you’ll need to install the necessary requirements. Here are the steps:

• Using pip:

pip install -r requirements.txt

• Using conda:

conda create -n crossvit python=3.8

conda activate crossvit

conda install pytorch=1.7.1 torchvision cudatoolkit=11.0 -c pytorch -c nvidia

pip install -r requirements.txt

Data Preparation

Next, you’ll need to prepare your data. Download and extract the ImageNet training and validation images from ImageNet. The directory structure should follow the standard layout required by torchvision.datasets.ImageFolder. Ensure your folders are set up like this:

pathtoimagenet
    train
        class1
            img1.jpeg
        class2
            img2.jpeg
    val
        class1
            img3.jpeg
        class2
            img4.jpeg

Loading Pretrained Models

You can utilize models that have been trained on ImageNet1K. Check out the available models here. To load pretrained weights, simply add the --pretrained flag to your command.

Training Your Model

To train the crossvit_9_dagger_224 model on a single node with 8 GPUs for 300 epochs, execute the following command:

python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py 
--model crossvit_9_dagger_224 --batch-size 256 --data-path pathtoimagenet

You can find other model names in modelscrossvit.py.

Multinode Training

For distributed training via Slurm or submitit, if you want to train the crossvit_9_dagger_224 model on 4 nodes with 8 GPUs each for 300 epochs, run:

python run_with_submitit.py --nodes 4 --model crossvit_9_dagger_224 
--data-path pathtoimagenet --batch-size 128 --warmup-epochs 30

Alternatively, you can start processes on each machine manually. Here’s how:

• Machine 0:

python -m torch.distributed.launch --nproc_per_node=8 --master_addr=MACHINE_0_IP 
--master_port=AVAILABLE_PORT --nnodes=2 --node_rank=0 main.py 
--model crossvit_9_dagger_224 --batch-size 256 --data-path pathtoimagenet

• Machine 1:

python -m torch.distributed.launch --nproc_per_node=8 --master_addr=MACHINE_0_IP 
--master_port=AVAILABLE_PORT --nnodes=2 --node_rank=1 main.py 
--model crossvit_9_dagger_224 --batch-size 256 --data-path pathtoimagenet

Note that some Slurm configurations might require adjustments depending on your cluster setup.

Evaluation

To evaluate a pretrained model on crossvit_9_dagger_224, use the following command:

python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py 
--model crossvit_9_dagger_224 --batch-size 128 --data-path pathtoimagenet --eval --pretrained

Troubleshooting

If you encounter issues during installation or execution, consider the following troubleshooting tips:

• Ensure that all dependencies in requirements.txt are installed correctly.
• Verify that your data directory is structured properly for image loading.
• If you run into memory issues, try reducing the batch size.
• Check your GPU configurations and ensure they are accessible.
• If using distributed training, double-check the master node’s IP and port settings.

For more insights, updates, or to collaborate on AI development projects, stay connected with **[fxis.ai](https://fxis.ai)**.

At **[fxis.ai](https://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.

Understanding Code with an Analogy

Think of using CrossViT as navigating a culinary journey in a bustling kitchen. Each step in the process represents a different technique or ingredient that you must prepare in a specific order to create a gourmet dish.

• Installation: This is akin to gathering all your ingredients and tools before cooking. You wouldn’t start a recipe without having everything ready!
• Data Preparation: This is like cutting and preparing vegetables. If these aren’t arranged neatly (or are missing), your cooking process will suffer.
• Loading Pretrained Models: You can think of this as following a trusted recipe. Instead of inventing the dish from scratch, you’re using a well-tested method to ensure success.
• Training Your Model: This part is similar to the actual cooking. It’s where you put everything together, apply heat, and let the flavors blend over time.
• Multinode Training: Imagine a culinary competition where multiple chefs (nodes) work together, each in their domain (kitchen), to create various components of one big feast.
• Evaluation: Finally, tasting the dish to see if it meets standards before serving it to guests. It’s all about ensuring what you created tastes great and works as intended.

By following these structured steps and understanding the process, you’ll navigate the complexities of CrossViT like a seasoned chef mastering a remarkable recipe!