Catalog / Caffe Deep Learning Framework Cheatsheet
Caffe Deep Learning Framework Cheatsheet
A quick reference guide for Caffe, covering its architecture, layers, tools, and usage for deep learning tasks.
Core Concepts & Architecture
Caffe Components
Blob |
The standard array/structure to store, communicate, and manipulate the actual data. It hides the computation and memory overhead of synchronization and communication from the user. |
Net |
A Net is composed of interconnected layers which describe a complete model. The net defines the data flow from layer to layer and stores the intermediate results. |
Layer |
The basic building block of a model. Layers take blobs as input and output blobs. They encapsulate all model parameters (weights) and computation. |
Solver |
Caffe drives learning and prediction by the |
Data Layer
The data layer sits at the bottom of a Caffe model. It is responsible for efficiently reading in data from disk, transforming it, and loading it into the network.
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Common Layers
Convolution Layer |
Applies a convolution filter to the input. Parameters: |
Pooling Layer |
Performs pooling (e.g., max or average) over the input. Parameters: |
ReLU Layer |
Applies the rectified linear unit activation function (max(0, x)). |
InnerProduct Layer |
Also known as a fully connected layer. Parameters: |
Softmax Layer |
Applies the softmax function to produce a probability distribution over classes. |
Defining Models with Protobuf
Net Definition (prototxt)
Caffe models are defined using protocol buffer (protobuf) text files ( Example Net Definition:
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Layer Definition
Each layer in the network is defined by a
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Solver Definition (prototxt)
The solver prototxt file defines the optimization parameters for training the network. Example Solver Definition:
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Caffe Command Line Tools
Training a Model
Use the
Options:
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Testing a Model
You can test a trained model using
Options:
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Converting Data
Caffe often uses LMDB or LevelDB databases for efficient data storage. The
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Python Interface
Basic Usage
Caffe provides a Python interface for model definition, training, and inference.
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Working with Blobs
Blobs are accessed via
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Custom Layers (Python)
Caffe allows you to define custom layers in Python. You need to define Example:
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