  Fire disasters typically result in lot of loss to life and property. It is
therefore imperative that precise, fast, and possibly portable solutions to
detect fire be made readily available to the masses at reasonable prices. There
have been several research attempts to design effective and appropriately
priced fire detection systems with varying degrees of success. However, most of
them demonstrate a trade-off between performance and model size (which decides
the model's ability to be installed on portable devices). The work presented in
this paper is an attempt to deal with both the performance and model size
issues in one design. Toward that end, a `designed-from-scratch' neural
network, named FireNet, is proposed which is worthy on both the counts: (i) it
has better performance than existing counterparts, and (ii) it is lightweight
enough to be deploy-able on embedded platforms like Raspberry Pi. Performance
evaluations on a standard dataset, as well as our own newly introduced
custom-compiled fire dataset, are extremely encouraging.
