| Pytorch: An imperative style, high-performance deep learning library A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... Advances in neural information processing systems 32, 2019 | 55210 | 2019 |
| End-to-end object detection with transformers N Carion, F Massa, G Synnaeve, N Usunier, A Kirillov, S Zagoruyko European conference on computer vision, 213-229, 2020 | 16350 | 2020 |
| Training data-efficient image transformers & distillation through attention H Touvron, M Cord, M Douze, F Massa, A Sablayrolles, H Jégou International conference on machine learning, 10347-10357, 2021 | 7905 | 2021 |
| Detectron2 Y Wu, A Kirillov, F Massa, WY Lo, R Girshick | 3165 | 2019 |
| Dinov2: Learning robust visual features without supervision M Oquab, T Darcet, T Moutakanni, H Vo, M Szafraniec, V Khalidov, ... arXiv preprint arXiv:2304.07193, 2023 | 2982* | 2023 |
| Mlperf inference benchmark VJ Reddi, C Cheng, D Kanter, P Mattson, G Schmuelling, CJ Wu, ... 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020 | 674* | 2020 |
| Detectron2. 2019 Y Wu, A Kirillov, F Massa, WY Lo, R Girshick | 454 | 2019 |
| Pytorch: An imperative style, high-performance deep learning library. arXiv 2019 A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... arXiv preprint arXiv:1912.01703 10, 1912 | 320 | 1912 |
| maskrcnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch F Massa, R Girshick | 287 | 2018 |
| PyTorch: an imperative style A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... High-Performance Deep Learning Library 12, 2019 | 191 | 2019 |
| Hybrid transformers for music source separation S Rouard, F Massa, A Défossez ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 184 | 2023 |
| xformers: A modular and hackable transformer modelling library B Lefaudeux, F Massa, D Liskovich, W Xiong, V Caggiano, S Naren, M Xu, ... R, 2022 | 167 | 2022 |
| NeurIPS A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... Springer, 2019 | 143* | 2019 |
| Deep exemplar 2d-3d detection by adapting from real to rendered views F Massa, BC Russell, M Aubry Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 120 | 2016 |
| Crafting a multi-task cnn for viewpoint estimation M Francisco, M Renaud, A Mathieu Proceedings of the British Machine Vision Conference, 91.1-91.12, 2016 | 99* | 2016 |
| Frame interpolation with multi-scale deep loss functions and generative adversarial networks J Van Amersfoort, W Shi, A Acosta, F Massa, J Totz, Z Wang, J Caballero arXiv preprint arXiv:1711.06045, 2017 | 55 | 2017 |
| Computer Vision–ECCV 2020 N Carion, F Massa, G Synnaeve, N Usunier, A Kirillov, S Zagoruyko, ... Proceedings of the 16th European Conference, Glasgow, UK, 23-28, 2020 | 48 | 2020 |
| Flash-decoding for long-context inference T Dao, D Haziza, F Massa, G Sizov URL: https://pytorch. org/blog/flash-decoding/,(Accessed: Feb. 3, 2024), 2023 | 27 | 2023 |
| Convolutional neural networks for joint object detection and pose estimation: A comparative study F Massa, M Aubry, R Marlet arXiv preprint arXiv:1412.7190, 2014 | 24 | 2014 |
| Advances in Neural Information Processing Systems 32 P Adam, G Sam, M Francisco, L Adam, B James, C Gregory Curran Associates, Inc, Red Hook, NY, 2019 | 20 | 2019 |
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