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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.
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| 2026-07-24 | 5242 |
| 2026-07-25 | 5242 |
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| 2026-08-06 | 5242 |
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# Yet Another EfficientDet Pytorch The pytorch re-implement of the official [EfficientDet](https://github.com/google/automl/tree/master/efficientdet) with SOTA performance in real time, original paper link: <https://arxiv.org/abs/1911.09070> ## Having troubles training? I might train it for you If you have troubles training a dataset, and if you are willing to share your dataset with the public or it's open already, post it on Issues with `help wanted` tag, I might try to help train it for you, if I'm free, which is not guaranteed. Requirements: 1. The total number of the image of the dataset should not be larger than 10K, capacity should be under 5GB, and it should be free to download, i.e. baiduyun. 2. The dataset should be in the format of this repo. 3. If you post your dataset in this repo, it is open to the world. So PLEASE DO NOT upload your confidential datasets! 4. If the datasets are against the law or invade one's privacy, feel free to contact me to delete it. 5. Most importantly, you can't demand me to train unless I wanted to. I'll post the trained weights in this repo along with the evaluation result. Hope it help whoever wants to try efficientdet in pytorch. Training examples can be found here. [tutorials](tutorial/). The trained weights can be found here. [weights](https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch/releases/tag/custom_datasets) ## Performance ## Pretrained weights and benchmark The performance is very close to the paper's, it is still SOTA. The speed/FPS test includes the time of post-processing with no jit/data precision trick. | coefficient | pth_download | GPU Mem(MB) | FPS | Extreme FPS (Batchsize 32) | mAP 0.5:0.95(this repo) | mAP 0.5:0.95(official) | | :-----: | :-----: | :------: | :------: | :------: | :-----: | :-----: | | D0 | [efficientdet-d0.pth](https://github.com/zylo117/Yet-Another-Efficient-Pytorch/releases/download/1.0/efficientdet-d0.pth) | 1049 | 36.20 | 163.14 | 33.1 | 33.8 | D1 | [efficientdet-d1.pth](https://github.com/zylo117/Yet-Another-Efficient-Pytorch/releases/download/1.0/efficientdet-d1.pth) | 1159 | 29.69 | 63.08 | 38.8 | 39.6 | D2 | [efficientdet-d2.pth](https://github.com/zylo117/Yet-Another-Efficient-Pytorch/releases/download/1.0/efficientdet-d2.pth) | 1321 | 26.50 | 40.99 | 42.1 | 43.0 | D3 | [efficientdet-d3.pth](https://github.com/zylo117/Yet-Another-Efficient-Pytorch/releases/download/1.0/efficientdet-d3.pth) | 1647 | 22.73 | - | 45.6 | 45.8 | D4 | [efficientdet-d4.pth](https://github.com/zylo117/Yet-Another-Efficient-Pytorch/releases/download/1.0/efficientdet-d4.pth) | 1903 | 14.75 | - | 48.8 | 49.4 | D5 | [efficientdet-d5.pth](https://github.com/zylo117/Yet-Another-Efficient-Pytorch/releases/download/1.0/efficientdet-d5.pth) | 2255 | 7.11 | - | 50.2 | 50.7 | D6 | [efficientdet-d6.pth](https://github.com/zylo117/Yet-Another-Efficient-Pytorch/releases/download/1.0/efficientdet-d6.pth) | 2985 | 5.30 | - | 50.7 | 51.7 | D7 | [efficientdet-d7.pth](https://github.com/zylo117/Yet-Another-Efficient-Pytorch/releases/download/1.2/efficientdet-d7.pth) | 3819 | 3.73 | - | 52.7 | 53.7 | D7X | [efficientdet-d8.pth](https://github.com/zylo117/Yet-Another-Efficient-Pytorch/releases/download/1.2/efficientdet-d8.pth) | 3983 | 2.39 | - | 53.9 | 55.1 ## Update Log [2020-07-23] supports efficientdet-d7x, mAP 53.9, using efficientnet-b7 as its backbone and an extra deeper pyramid level of BiFPN. For the sake of simplicity, let's call it efficientdet-d8. [2020-07-15] update efficientdet-d7 weights, mAP 52.7 [2020-05-11] add boolean string conversion to make sure head_only works [2020-05-10] replace nms with batched_nms to further improve mAP by 0.5~0.7, thanks [Laughing-q](https://github.com/Laughing-q). [2020-05-04] fix coco category id mismatch bug, but it shouldn't affect training on custom dataset. [2020-04-14] fixed loss function bug. please pull the latest code. [2020-04-14] for those who needs help or can't get a good result after severa
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:fdddab0c41e94428, topic:pytorch
matched fp:fdddab0c41e94428, topic:object-detection