select model

[mahdikhashan]

• decision metrics

[mahdikhashan]

I implemented XOR and MNIST using MLP. also with some chat with GPT-o1, it suggested to have a small Model like MobileNet or SqueezeNet with TransferLearning to on some database to have a classification (for example cat vs dogs).

current decisions metrics are "time to train/learn", "should be classification problem or sth that can be used with a UI", "play with hyperparameters".

[mahdikhashan]

For an image classification model that can be trained in about 1 minute, you would need to use a very lightweight model and a small dataset. Here are a few options:

1. MobileNetV2

• Why: MobileNetV2 is designed to be efficient with a small number of parameters while maintaining a reasonable level of accuracy. It's commonly used in mobile and embedded devices, making it well-suited for quick training on small datasets.
• Training Time: MobileNetV2 can be trained on small datasets (a few hundred to a thousand images) in under a minute, especially if you're using transfer learning (starting with pre-trained weights).
• Dataset Size: A small dataset (e.g., 200–1,000 images with a couple of classes, like cat vs. dog) can work well for training in under 1 minute.

2. SqueezeNet

• Why: SqueezeNet is another very lightweight model designed for quick training and low computational resource usage. It achieves competitive accuracy with fewer parameters.
• Training Time: SqueezeNet can also train on small datasets in under a minute when fine-tuned with pre-trained weights.
• Dataset Size: Similar to MobileNetV2, it works well with small datasets (100–500 images).

3. EfficientNet-Lite

• Why: EfficientNet-Lite is a lighter version of EfficientNet designed to balance model size and accuracy, making it suitable for tasks requiring fast training.
• Training Time: Depending on the hardware and dataset, training EfficientNet-Lite on small datasets can be done in under a minute with transfer learning.
• Dataset Size: With 100–1,000 images, training on EfficientNet-Lite can be fast enough for 1-minute training.

4. Custom CNN (Small Architecture)

• Why: You can also create a small custom convolutional neural network (CNN) with only a few layers (e.g., 2-3 convolutional layers followed by a dense layer).
• Training Time: A very small CNN can be trained in under a minute, especially when using transfer learning from pre-trained models like those trained on CIFAR-10.
• Dataset Size: A few hundred images should be sufficient.

Key Factors for Training in 1 Minute:

• Use Transfer Learning: Start with pre-trained weights from models like MobileNetV2, EfficientNet, or SqueezeNet. This significantly reduces training time because the model already knows useful features.
• Small Dataset: Keep the dataset small, ideally under 1,000 images.
• Small Image Resolution: Use smaller image resolutions (e.g., 32x32 or 64x64) to speed up the training process.
• Minimal Epochs: Train for just a few epochs (1-10 epochs) since you're aiming for rapid results rather than full optimization.

With these strategies, you can train a simple image classification model in under a minute. Keep in mind that this fast training time typically sacrifices accuracy compared to training for longer periods, but for quick prototypes or basic tasks, it works well.

[mahdikhashan]

I can also use checkpoints feature and keep training.

[mahdikhashan]

I can also benefit from distributed training

[mahdikhashan]

maybe I can do hyperparameter tunning and compare two models?? CNN and MLP

[mahdikhashan]

I'm going to use mnist with mlp and cnn, training multiple models, comparing the results and registering all in an object storage, probably use mlflow for experiment tracking.