What are the most effective ways to choose a pre-trained model for your deep learning task?

The most helpful way to identify the ideal pre-trained model is to follow a methodical process to identify the ideal solution. Step 1: Define the task at hand and identify the problem that is to be solved. Step 2: Identify the models trained for the specific task at hand. Step 3: Choose the desired the model size for the problem depending on how complex or simple the problem is Step 4: Check the context of he model or the data on which it is trained. It has to be similar to the data it is going to be applied to Step 5: Check for ability to fine tune if needed to solve. This will ensure retained already learned information while allowing it to be more revenant to the task at hand.

Transfer learning is a way to leverage the knowledge gained by a network trained on a large dataset and use it in a new application similar to the previous one. A few things to consider: - The similarity between the dataset you have and the dataset that the model has been trained on previously. It doesn't make sense to use transfer learning on a network trained on X-ray images and your dataset has images for cars and streets. - before starting fine-tuning the model, consider using zero-shot learning. - You can consider model pruning as well, where you can prune the original model to make it smaller.

One most important factor to consider is the resilience of the model towards the noisy data. Certain models generalize well and noise-tolerant than others. This noise-tolerance may not necessarily proportional to number of billions of trainable params. Often, tweaking the drop-out probability as well as the type of normalization plays a vital role in improving the resilience.

There is always a trade-off between performance and complexity. Generally speaking, the more complex the model the more accuracy you will get, however, that comes with the cost. If you have two models, one with high complexity, high cost to serve high latency, and high accuracy, and on the other hand, you have a smaller one with less complexity, low latency, and acceptable accuracy, then definitely go with the second one.

The selection of model architecture directly impacts the effectiveness of the pre-trained model for the specific task. DL models come in a wide spectrum of complexities, ranging from relatively shallow architectures like convolutional neural networks (CNNs) to intricate models such as recurrent neural networks (RNNs) and transformer models. Simpler architectures are suitable for tasks with limited data and straightforward patterns, as they are less prone to overfitting. On the other hand, complex models like transformers excel at capturing intricate dependencies in extensive datasets. Checks on available computational resources, including processing power and memory, is important.

While there are many architectures to choose from the ultimate chose depends on the final use case. The most popular architectures are 1) CNNs for Image and Video recognition tasks 2) RNN is useful for NLP that is useful for sequential data like Language generation, weather and stock predictions etc 3) Transformers: This is the most versatile architecture which uses parallel processing.This 3 further sub classes a) Encoder Only Models - Useful for tasks Word classification and sentiment analysis b) Decoder Only Models - Useful for text and code generation etc c) Encoder & Decoder Models - Useful for tasks like Translation, Summarisation etc

AI and ML models are all about predictions. whether it's next token prediction, image class prediction, word class prediction, etc... The goal is to become as accurate as possible. However, different applications use different metrics. For example, email spam applications are precision-oriented as the goal is to identify as many true spam emails as possible. On the other hand, in the healthcare field, the ML models usually are recall-oriented, where missing a true positive can be expensive.

Practical assessment relies on standard evaluation metrics like accuracy, precision, recall, F1-score, and mean average precision (mAP). However, a critical practicality is the consideration of the trade-offs between these metrics. DS must make informed choices by prioritizing the metrics relevant to given task. E.g. in object detection, the focus might shift towards mAP as the primary, as it accurately gauges the model's capability to not only identify but also locate & classify multiple objects within an image. Such practical evaluation ensures that the selected pre-trained model aligns with the performance requirements and sheds light on its impact when applied to the new dataset, enabling a data-driven & informed selection process

Nowadays, open source is eating the world. Finding open-source models is no longer an issue using hubs like as Hugging Face. However, you should always check the license of the model. Some models come with a license for research purposes only. Others come with commercial and research licenses. Moreover, some of them can come with a really tricky license. For instance, you can use the model in production, but you should have less than 100K active users, otherwise, you have to pay the model's company provider a fee.

It is really important to choose a flexible model. In most cases, you would have a different number of classes, or sometimes you want to use the model as part of another model. For example, one time I used a pre-trained Resnet model on Imagnet to be my encoder, and then I used a network with pre-trained embedding as a decoder. Then I finetuned the model (encoder and decoder) for my image captioning task