Object Detection using caffe
Object detection in computer vision is as simple as it looks i.e. detect an object, predict it, and identify its region. Object detection is based on image classification. Based on this, we can perform recognition to find all possible objects in a given frame Object recognition is a computer vision technique for identifying objects in images or videos.
These are the classes in which caffe model is use to classify object
SSD Mobilenet network
The MobileNet model is based on depth-separated convolutions, which are factor-based convolutions.
They multiply the standard convolution into a deep circle and a 1 × 1 convolution called a point convolution.
In MobileNet networks, depth convolution uses one filter for each input channel. Point convolution then uses a 1 × 1 convolution to combine the results of the depth convolution.
Standard convolution filters and combines the inputs into a new set of outputs in one step.
Deep-separation circuitry divides it into two layers — a separate layer for filtering and a separate layer for combining.
This partitioning of factors significantly reduces computation and model size.
The architecture of the SSD is a single convolutional network that learns to predict interface locations and classify those locations once.
Therefore, SSDs can be trained from end to end.
Architecture
Deep learning with caffe framework:
Caffe is a deep learning framework developed by Berkeley AI Research and community contributors. Caffe was developed as a faster and more efficient alternative to other object detection frameworks. Caffe can process 60 million images per day on a single NVIDIA K- 0 GPU. It is 1ms per frame for inference and ms per frame for learning It has a modern structure in both code and model. In terms of speed, it can process more than 60 million images per day with a single NVIDIA K 0 GPU * processor. It is currently working in academic research projects, commercial prototypes, and even industrial applications in a large-scale field of view. It represents a convolutional architecture for fast function embedding and is written as a BSD-licensed C library with Python and MATLAB bindings. It is used to efficiently train and deploy general-purpose convolutional neural networks in commodity architectures. The architecture of the above framework is generally divided into the following parts:
. Data storage:
It uses N-dimensional matrix data in a C-adjacent way called blobs to store and transfer data. A patch can be thought of as an abstraction layer between the CPU and the GPU. The CPU data is loaded into a blob, which is then passed to the GPU for computation. Under the hood, the blob uses the SyncedMem class to sync CPU and GPU values. The blob is then advanced to the next level, ignoring the lower implementation details, thus maintaining high efficiency. To ensure efficient use of memory, a lazy demand allocation technique is used to reserve memory as needed for the host and device. LevelDB databases are used for large scale data. Deep learning models are stored in secondary storage such as Google log buffers, which provide efficient serialization, human-readable text formatting, etc. 2. Levels:
Blobs are passed to it as input and produced accordingly. It follows many, many relationships. It has the following main responsibilities as part of the model operations:
Configuration: This initializes the layer and the connections below it for the first time during model initialization.
Forward: Inputs are passed and corresponding outputs are created.
Backpropagation: Computing gradients with respect to the output, model hyperparameters, and inputs, which are then propagated to subsequent levels using the backpropagation technique.
It offers various layer settings such as Convolution, Pooling, non-linear activations such as Rectified, Linear Units (ReLU) and widely used optimization losses such as Log Loss, R-squared, SoftMax, etc. Layers can be expanded to a new custom user layer. implementation using the component structure of networks.
3. Networks and their underlying driving model It uses a data structure called a directed acyclic graph to store the operations performed by the layers below, ensuring the correctness of forward and backward movement. A typical Caffe model network starts with a data layer that loads data from disk and ends with a lossless layer according to application requirements. It can run on CPU/GPU and switching between them is seamless and modelless.
.Training the network A typical Caffe model is trained using a fast and standard stochastic gradient descent algorithm. Data can be processed into mines that move through the network one by one. Important training related parameters such as learning rate deceleration graphs, tempo and stop and restart control points are implemented with very detailed documentation. It also supports fine-tuning, a technique where an existing model can be used to support a new architecture or data. The weights of the previous models are updated for the new application and new weights are assigned when necessary. This technique is widely used in many real-world deep learning applications.
Advantages of Caffe Deep Learning Framework :
It provides complete training, testing, tuning and model deployment. It provides many examples of the above tasks. Previously used for visual tasks, its users have now adopted it for other deep learning applications such as speech recognition, neural networks and robotics. It can be used on cloud-based platforms with seamless platform switching. Modularity: can be extended to new data, layers and loss optimization functions. It has reference examples with applied layers and loss functions.
Speed: It can be used to process 60 million images per day with NVIDIA CUDA K 0 GPU. It is one of the fastest convnet apps on the market.
Layering Architecture and Implementation: Model definitions are written in the protocol proxy language as configuration files. The network architecture uses a directed acyclic graph. Once a model is instantiated, it allocates the exact memory that the model needs. Moving from a CPU-based environment to a GPU environment requires a single function call.
Test coverage: Each of its modules is tested, and its open source project does not allow any module to be linked without corresponding tests, which allows rapid improvements and rework of the code base. Thus, it increases its maintainability relatively without bugs/defects. Python and MATLAB support for debugging: Provides interface and ease of use with existing research framework used by research institutions. Both languages can be used to create a network and classify inputs. Python in Layering also allows you to use the solution module to develop new training techniques and make them easier to use. previously trained models used as reference:
It provides reference models for research and scientific projects, such as ImageNet trained models, etc. Thus, it provides a common software component that can be extended to achieve rapid success in developing model architectures for real-life applications. It differs from other modern CNN frameworks in the following ways: The implementation is primarily C-based, so it can be easily integrated with existing C systems and common industry interfaces. In addition, CPU mode removes the barrier of having a dedicated hardware platform to execute and test the model after the model is trained. Benchmarks are ready off the shelf for quick testing with the latest results. Thus, it reduces the cost of retraining.
Conclusion
The Caffe Deep Learning Framework is constantly evolving because it is open source and well documented. Many developers have forked this Github repository. So, many important changes were brought to it. Recently, Caffe 2 was developed and integrated with the PyTorch deep learning GitHub repository.
To learn more about caffe