Difference between Pretrained model and native model for portable object detector

Portable object detectors are becoming increasingly popular due to their versatility and ease of use. They are used in a wide range of applications, including autonomous vehicles, surveillance cameras, industrial inspection systems, and mobile apps for augmented reality, gaming, and social media.

So let’s first define:

Q1) What is a portable Object Detector

A portable object detector is a type of computer vision system that can detect and identify objects in images or video streams in real time, and can be easily deployed on a variety of hardware platforms.

Unlike traditional object detection models that require a powerful computer or server to run, portable object detectors are designed to work efficiently on small devices such as smartphones, tablets, drones, and other embedded systems. They typically use lightweight and efficient algorithms, which are optimized for resource-constrained devices, such as mobile CPUs or GPUs.

Great! Now how to do get it to work? Most data scientists would encourage the beginners to start with a pre-trained model. So what is a pre-trained model?

2) What is a pretrained model?

A pretrained model is a machine learning model that has been trained on a large dataset by its creators or other third-party organizations. This training is typically done on a large compute cluster or specialized hardware and can take days or even weeks to complete. The resulting model is then made available for use by others, who can fine-tune the model on their own data or use it as-is for various applications.

In contrast, a native model is a machine learning model that is trained specifically for a particular task or dataset by the user themselves. The user trains the model from scratch using their own data and hardware resources.

The main advantage of using a pretrained model is that it can save time and resources for the user. Training a machine learning model from scratch can be a time-consuming and computationally expensive process, and a pretrained model can provide a good starting point that can be fine-tuned on the user’s data. In addition, a pretrained model may have already learned useful features that can be difficult to learn from scratch.

Can elaborate more on the Native model? Can we code it in Python for example?

3) What is Native model?

A native model allows the user to have more control over the training process and the resulting model. The user can tailor the model to their specific needs and have more confidence in the model’s performance on their particular task or dataset.

Overall, the choice between a pretrained model and a native model depends on the specific needs of the user and the resources available.

In the context of machine learning, the term “native model” can refer to a model that is developed using the tools and frameworks that are built into a particular programming language or software environment.

In Python, one of the most popular machine learning libraries is TensorFlow, which provides a range of tools for building and training deep learning models. A native model in TensorFlow would be one that is developed using the library’s built-in functions and modules.

Tensorflow is of course very popular. It is great to know that we can develop our own native model using Tensorflow library. Possible to give some examples of pretrained model, also developed by Google?

4) Example of pretrained model: MobileNet

MobileNet is a family of deep neural network models designed for efficient on-device computation on mobile and embedded devices. These models were developed by Google researchers as part of their efforts to make deep learning models more accessible and efficient for mobile devices.

MobileNet models are designed to be computationally efficient while still maintaining high accuracy. They achieve this through a combination of several techniques, including depthwise separable convolutions, which separate the spatial and channel-wise convolutions in a standard convolution layer, and linear bottlenecks, which reduce the dimensionality of the feature maps. These techniques allow MobileNet models to have a smaller number of parameters and computations compared to other neural networks while still achieving good accuracy.

MobileNet models have been used for a variety of tasks, including image classification, object detection, and semantic segmentation, and have been deployed on a wide range of mobile and embedded devices, including smartphones, drones, and robots.

Another example, perhaps?

5) Example of Google model: InceptionNet

InceptionNet, also known as Inception V1, is a deep convolutional neural network (CNN) architecture designed for image classification tasks. It was introduced in a research paper by Google researchers in 2014, and was one of the first models to incorporate the idea of “inception modules”, which are designed to improve the efficiency of convolutional networks.

The inception modules consist of a set of parallel convolutional layers of different sizes (1x1, 3x3, and 5x5) and a pooling layer, all of which are concatenated along the depth dimension. This allows the network to learn features at multiple scales, which helps to improve the overall performance of the model.

InceptionNet also incorporates a technique called “batch normalization”, which normalizes the activations of each layer to improve the stability and speed of training. Additionally, the model uses a global average pooling layer instead of a fully connected layer, which helps to reduce overfitting and improve the generalization performance of the model.

Overall, InceptionNet was a significant breakthrough in the field of deep learning, and it has inspired many subsequent models that use similar techniques to achieve state-of-the-art performance on various image classification tasks.

Sounds really great! There are some many pretrained models to help us, as well as the possibility to create our own models. Now what is the actual goal of pretrained and native model? How can we easily improve accuracy once we have found our ideal model?

7) Ultimate goal: improve accuracy by adjusting weights and bias

In machine learning, the weights and biases of a model are key parameters that determine the accuracy and performance of the model. Here are some strategies for improving the weights and biases of a machine learning model:

a) Adjust the learning rate: The learning rate is a parameter that determines how much the weights and biases are updated during each iteration of the training process. If the learning rate is too high, the weights and biases may oscillate or diverge, leading to poor performance. If the learning rate is too low, the model may converge too slowly or get stuck in local minima. Experimenting with different learning rates can help to optimize the weights and biases of the model.

b) Use regularization: Regularization is a technique that adds a penalty term to the loss function of the model, which helps to prevent overfitting and improve generalization. There are several types of regularization, including L1 and L2 regularization, dropout, and early stopping.

c) Increase the number of training samples: The performance of a machine learning model often improves as the size of the training set increases. By increasing the number of training samples, the model has more data to learn from, which can help to improve the weights and biases.

d) Try different network architectures: Different neural network architectures can have a significant impact on the performance of a machine learning model. Experimenting with different architectures, such as the number of layers, the number of nodes in each layer, and the activation functions, can help to optimize the weights and biases.

e) Use pre-trained models: Pre-trained models are neural networks that have been trained on large datasets and can be fine-tuned for specific tasks. By starting with a pre-trained model and adjusting the weights and biases for the specific task at hand, it is possible to achieve good performance with less training data and computational resources.

Summary

It is great to study a lot of models and watch lots of Youtube. Finally, I believe that in order to begin learning a machine language, whether deep learning or a simple convolution network, the user must first perform some basic coding. Even a few lines of Keras will do. Good luck with your coding and have fun!!!