QWID: Quantized Weed Identification Deep neural network

October 29, 2023 · Entered Twilight · 🏛 2024 IEEE 31st International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)

Repo contents: README.md, inference.py, prepare_data, requirements.txt, train_model.py

Authors Parikshit Singh Rathore arXiv ID 2310.18921 Category cs.CV: Computer Vision Cross-listed cs.AI Citations 1 Venue 2024 IEEE 31st International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW) Repository https://github.com/parikshit14/QNN-for-weed ⭐ 1 Last Checked 3 months ago

Abstract

In this paper, we present an efficient solution for weed classification in agriculture. We focus on optimizing model performance at inference while respecting the constraints of the agricultural domain. We propose a Quantized Deep Neural Network model that classifies a dataset of 9 weed classes using 8-bit integer (int8) quantization, a departure from standard 32-bit floating point (fp32) models. Recognizing the hardware resource limitations in agriculture, our model balances model size, inference time, and accuracy, aligning with practical requirements. We evaluate the approach on ResNet-50 and InceptionV3 architectures, comparing their performance against their int8 quantized versions. Transfer learning and fine-tuning are applied using the DeepWeeds dataset. The results show staggering model size and inference time reductions while maintaining accuracy in real-world production scenarios like Desktop, Mobile and Raspberry Pi. Our work sheds light on a promising direction for efficient AI in agriculture, holding potential for broader applications. Code: https://github.com/parikshit14/QNN-for-weed