Dhruv Sheth / terrain-classification-spresense
This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.
About this project
A few days ago Sony announced Spresense capabilities and test procedures on how Spresense will be useful in Space Applications and Satellite Monitoring Systems. While currently this board will be used on ongoing missions or to develop existing technology systems, it might prove of great help in expanding core research in the EdgeAI sector. With opening avenues in low power consumption monitoring systems and software under constrained computational power, further this will help in democratizing Aerospace based terrain monitoring or in that matter, even space exploration.
This project proposes, a faster, accurate, cheaper method to satellite terrain classification on Sony Spresense using EdgeImpulse
Download block output
|Image training data||NPY file||650 windows|
|Image training labels||NPY file||650 windows|
|Image testing data||NPY file||150 windows|
|Image testing labels||NPY file||150 windows|
|Transfer learning model||TensorFlow Lite (float32)||427 KB|
|Transfer learning model||TensorFlow Lite (int8 quantized)||285 KB|
|Transfer learning model||TensorFlow SavedModel||685 KB|
Data collected800 items
|License||No license attached|