Image Model Blocks

A Fire Module is a building block for convolutional neural networks, notably used as part of SqueezeNet. A Fire module is comprised of: a squeeze convolution layer (which has only 1x1 filters), feeding into an expand layer that has a mix of 1x1 and 3x3 convolution filters. We expose three tunable dimensions (hyperparameters) in a Fire module: $s_{1x1}$, $e_{1x1}$, and $e_{3x3}$. In a Fire module, $s_{1x1}$ is the number of filters in the squeeze layer (all 1x1), $e_{1x1}$ is the number of 1x1 filters in the expand layer, and $e_{3x3}$ is the number of 3x3 filters in the expand layer. When we use Fire modules we set $s_{1x1}$ to be less than ($e_{1x1}$ + $e_{3x3}$), so the squeeze layer helps to limit the number of input channels to the 3x3 filters.

Source: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size


Paper Code Results Date Stars


Task Papers Share
Object Detection 11 11.46%
General Classification 11 11.46%
Image Classification 8 8.33%
Classification 7 7.29%
Face Recognition 3 3.13%
Face Verification 3 3.13%
Quantization 3 3.13%
Specificity 3 3.13%
Object Recognition 3 3.13%