參考文獻 |
[1] Y. Abadade, A. Temouden, H. Bamoumen, N. Benamar, Y. Chtouki, and A. S. Hafid, "A comprehensive survey on tinyml," IEEE Access, 2023.
[2] B. Murdoch, "Privacy and artificial intelligence: challenges for protecting health information in a new era," BMC Medical Ethics, vol. 22, pp. 1-5, 2021.
[3] Z. Zou, K. Chen, Z. Shi, Y. Guo, and J. Ye, "Object detection in 20 years: A survey," Proceedings of the IEEE, vol. 111, no. 3, pp. 257-276, 2023.
[4] Ultralytics. (March 13). Available: https://github.com/ultralytics/ultralytics
[5] J. Lin, L. Zhu, W.-M. Chen, W.-C. Wang, C. Gan, and S. Han, "On-device training under 256kb memory," Advances in Neural Information Processing Systems, vol. 35, pp. 22941-22954, 2022.
[6] R. David, J. Duke, A. Jain, V. Janapa Reddi, N. Jeffries, J. Li, N. Kreeger, I. Nappier, M. Natraj, and T. Wang, "Tensorflow lite micro: Embedded machine learning for tinyml systems," Proceedings of Machine Learning and Systems, vol. 3, pp. 800-811, 2021.
[7] E. Impulse. (March 13). Available: https://edgeimpulse.com/
[8] E. Impulse. (March 2024). FOMO: Object detection for constrained devices. Available: https://docs.edgeimpulse.com/docs/edge-impulse-studio/learning-blocks/object-detection/fomo-object-detection-for-constrained-devices
[9] J. Moosmann, M. Giordano, C. Vogt, and M. Magno, "Tinyissimoyolo: A quantized, low-memory footprint, tinyml object detection network for low power microcontrollers," in 2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS), pp. 1-5, 2023.
[10] J. Moosmann, H. Mueller, N. Zimmerman, G. Rutishauser, L. Benini, and M. Magno, "Flexible and fully quantized ultra-lightweight tinyissimoyolo for ultra-low-power edge systems," arXiv preprint arXiv:2307.05999, 2023.
[11] C. White, M. Safari, R. Sukthanker, B. Ru, T. Elsken, A. Zela, D. Dey, and F. Hutter, "Neural architecture search: Insights from 1000 papers," arXiv preprint arXiv:2301.08727, 2023.
[12] Ultralytics. (March 13). YOLOv5. Available: https://github.com/ultralytics/yolov5
[13] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
[14] D. Blalock, J. J. Gonzalez Ortiz, J. Frankle, and J. Guttag, "What is the state of neural network pruning?," Proceedings of machine learning and systems, vol. 2, pp. 129-146, 2020.
[15] Huggingface. (March 13). Quantization. Available: https://huggingface.co/docs/optimum/concept_guides/quantization
[16] S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, "Image segmentation using deep learning: A survey," IEEE transactions on pattern analysis and machine intelligence, vol. 44, no. 7, pp. 3523-3542, 2021.
[17] TensorFlow. (March 13). Visual Wake Words with TensorFlow Lite Micro. Available: https://blog.tensorflow.org/2019/10/visual-wake-words-with-tensorflow-lite_30.html
[18] (March 13). MCUs Expected to Make Modest Comeback After 2020 Drop. Available: https://www.icinsights.com/news/bulletins/mcus-expected-to-make-modest-comeback-after-2020-drop--/
[19] TensorFlow. (March 13). TensorFlow Lite Model conversion overview. Available: https://www.tensorflow.org/lite/models/convert
[20] (March 13). TensorFlow lite interpreter "tf.lite.Interpreter". Available: https://www.tensorflow.org/api_docs/python/tf/lite/Interpreter
[21] (March 13). TensorFlow Lite inference. Available: https://www.tensorflow.org/lite/guide/inference
[22] (March 14). Quantization aware training of TensorFlow. Available: https://www.tensorflow.org/model_optimization/guide/quantization/training
[23] TensorFlow. (March 14). Post-training quantization of TensorFlow. Available: https://www.tensorflow.org/lite/performance/post_training_quantization
[24] (March 14). The PASCAL Visual Object Classes Homepage. Available: http://host.robots.ox.ac.uk/pascal/VOC/
[25] P. Henderson and V. Ferrari, "End-to-end training of object class detectors for mean average precision," in Computer Vision–ACCV 2016: 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part V 13, pp. 198-213, 2017.
[26] Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, "Xlnet: Generalized autoregressive pretraining for language understanding," Advances in neural information processing systems, vol. 32, 2019.
[27] D. H. Dario Amodei. (March 14). AI and compute. Available: https://openai.com/research/ai-and-compute
[28] J. Gou, B. Yu, S. J. Maybank, and D. Tao, "Knowledge distillation: A survey," International Journal of Computer Vision, vol. 129, no. 6, pp. 1789-1819, 2021.
[29] Y. He and L. Xiao, "Structured pruning for deep convolutional neural networks: A survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
[30] Z. Liao, V. Quétu, V.-T. Nguyen, and E. Tartaglione, "Can Unstructured Pruning Reduce the Depth in Deep Neural Networks?," in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1402-1406, 2023.
[31] L. Chen, Y. Chen, J. Xi, and X. Le, "Knowledge from the original network: restore a better pruned network with knowledge distillation," Complex & Intelligent Systems, pp. 1-10, 2021.
[32] (July 20). Awesome-Pruning. Available: https://github.com/he-y/Awesome-Pruning
[33] J. D. De Leon and R. Atienza, "Depth pruning with auxiliary networks for tinyml," in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3963-3967, 2022.
[34] F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong, and Q. He, "A comprehensive survey on transfer learning," Proceedings of the IEEE, vol. 109, no. 1, pp. 43-76, 2020.
[35] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, "Yolov4: Optimal speed and accuracy of object detection," arXiv preprint arXiv:2004.10934, 2020.
[36] (March 14). Ultralytics | Revolutionizing the World of Vision AI. Available: https://www.ultralytics.com/
[37] T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature pyramid networks for object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117-2125, 2017.
[38] S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, "Path aggregation network for instance segmentation," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8759-8768, 2018.
[39] K. He, X. Zhang, S. Ren, and J. Sun, "Spatial pyramid pooling in deep convolutional networks for visual recognition," IEEE transactions on pattern analysis and machine intelligence, vol. 37, no. 9, pp. 1904-1916, 2015.
[40] M. Qiu, L. Huang, and B.-H. Tang, "ASFF-YOLOv5: Multielement detection method for road traffic in UAV images based on multiscale feature fusion," Remote Sensing, vol. 14, no. 14, p. 3498, 2022.
[41] C. S. Wiki. (March 14). File:MaxpoolSample2.png - Computer Science Wiki. Available: https://computersciencewiki.org/index.php/File:MaxpoolSample2.png
[42] Ultralytics. (March 14). Github of Mosaic augmentation. Available: https://github.com/ultralytics/ultralytics/blob/5c1277113b19e45292c01e5a47aa2bdb6ebc98d0/ultralytics/data/augment.py#L133
[43] Ultralytics. (March 14). mosaic augmentation #1423.ultralytics/yolov5. Available: https://github.com/ultralytics/yolov5/issues/1423#issuecomment-1093947259
[44] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, "Focal loss for dense object detection," in Proceedings of the IEEE international conference on computer vision, pp. 2980-2988, 2017.
[45] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, "Microsoft coco: Common objects in context," in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pp. 740-755, 2014.
[46] C.-H. Chen, M.-Y. Lin, and X.-C. Guo, "High-level modeling and synthesis of smart sensor networks for Industrial Internet of Things," Computers & Electrical Engineering, vol. 61, pp. 48-66, 2017.
[47] P. Micikevicius, S. Narang, J. Alben, G. Diamos, E. Elsen, D. Garcia, B. Ginsburg, M. Houston, O. Kuchaiev, and G. Venkatesh, "Mixed precision training," arXiv preprint arXiv:1710.03740, 2017.
[48] ONNX. (06/11). Convert a PyTorch model to Tensorflow using ONNX. Available: https://github.com/onnx/tutorials/blob/main/tutorials/PytorchTensorflowMnist.ipynb
[49] TensorFlow. (06/11). Convert TF Object Detection API model to TFLite.ipynb. Available: https://colab.research.google.com/github/tensorflow/models/blob/master/research/object_detection/colab_tutorials/convert_odt_model_to_TFLite.ipynb#scrollTo=-ecGLG_Ovjcr
[50] C. Michaelis, B. Mitzkus, R. Geirhos, E. Rusak, O. Bringmann, A. S. Ecker, M. Bethge, and W. Brendel, "Benchmarking robustness in object detection: Autonomous driving when winter is coming," arXiv preprint arXiv:1907.07484, 2019.
[51] J. Moosmann, P. Bonazzi, Y. Li, S. Bian, P. Mayer, L. Benini, and M. Magno, "Ultra-efficient on-device object detection on ai-integrated smart glasses with tinyissimoyolo," arXiv preprint arXiv:2311.01057, 2023. |