Yet-Another-EfficientDet-Pytorch-master.rar

# 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> ## 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(paper) | | :-----: | :-----: | :------: | :------: | :------: | :-----: | :-----: | | 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/1.0/efficientdet-d7.pth) | 3819 | 3.73 | - | 51.2 | 52.2 ## Speed Test This pure-pytorch implement is up to 2 times faster than the official Tensorflow version without any trick. Recorded on 2020-04-26, official git version: <https://github.com/google/automl/commit/006668f2af1744de0357ca3d400527feaa73c122> | coefficient | FPS(this repo, tested on RTX2080Ti) | FPS(official, tested on T4) | Ratio | | :------: | :------: | :------: | :-----: | | D0 | 36.20 | 42.1 | 0.86X | | D1 | 29.69 | 27.7 | 1.07X | | D2 | 26.50 | 19.7 | 1.35X | | D3 | 22.73 | 11.8 | 1.93X | | D4 | 14.75 | 7.1 | 2.08X | | D5 | 7.11 | 3.6 | 1.98X | | D6 | 5.30 | 2.6 | 2.03X | | D7 | 3.73 | - | - | Test method (this repo): Run this test on 2080Ti, Ubuntu 19.10 x64. 1. Prepare a image tensor with the same content, size (1,3,512,512)-pytorch. 2. Initiate everything by inferring once. 3. Run 10 times with batchsize 1 and calculate the average time, including post-processing and visualization, to make the test more practical. ___ ## Update Log [2020-05-11] add boolean string convertion 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 several epochs, check out this [tutorial](tutorial/train_shape.ipynb). You can run it on colab with GPU support. [2020-04-10] warp the loss function within the training model, so that the memory usage will be balanced when training with multiple gpus, enabling training with bigger batchsize. [2020-04-10] add D7 (D6 with larger input size and larger anchor scale) support and test its mAP [2020-04-09] allow custom anchor scales and ratios [2020-04-08] add D6 support and test its mAP [2020-04-08] add training script and its doc; update eval script and simple inference script. [2020-04-07] tested D0-D5 mAP, result seems nice, details can be found [here](benchmark/coco_eval_result) [2020-04-07] fix anchors strategies. [2020-04-06] adapt anchor strategies. [2020-04-05] create this repository. ## Demo # install requirements pip install pycocotools numpy opencv-python tqdm tensorboard tensorboardX pyyaml webcolors pip install torch==1.4.0 pip install torchvision==0.5.0 # run the simple inference script python efficientdet_test.py ## Training Training EfficientDet is a painful and time-consuming task. You shouldn't expect to get a good result within a day or two. Please be patient. Check out this [tutorial](tutorial/train_shape.ipynb) if you are new to this. You can run it on colab with GPU support. ### 1. Prepare your dataset # your dataset structure should be like this datasets/ -your_project_name/ -train_set_name/ -*.jpg -val_set_name/ -*.jpg -annotations -instances_{train_set_name}.json -instances_{val_set_name}.json # for example, coco2017 datasets/ -coco2017/ -train2017/ -000000000001.jpg -000000000002.jpg -000000000003.jpg -val2017/ -000000000004.jpg -000000000005.jpg -000000000006.jpg -annotations -instances_train2017.json -instances_val2017.json ### 2. Manual set project's specific parameters # create a yml file {your_project_name}.yml under 'projects'folder # modify it following 'coco.yml' # for example project_name: coco train_set: train2017 val_set: val2017 num_gpus: 4 # 0 means using cpu, 1-N means using gpus # mean and std in RGB order, actually this part should remain unchanged as long as your dataset is similar to coco. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] # this is coco anchors, change it if necessary anchors_scales: '[2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)]' anchors_ratios: '[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]' # objects from all labels from your dataset with the order from your annotations. # its index must match your dataset's category_id. # category_id is one_indexed, # for example, index of 'car' here is 2, while category_id of is 3 obj_list: ['person', 'bicycle', 'car', ...] ### 3.a. Train on coco from scratch(not necessary) # train efficientdet-d0 on coco from scratch # with batchsize 12 # This takes time and requires change # of hyperparameters every few hours. # If you have months to kill, do it. # It's not like someone going to achieve # better score than the one in the paper. # The first few epoches will be rather unstable, # it's quite normal when you train from scratch. python train.py -c 0 --batch_size 64 --optim sgd --lr 8e-2 ### 3.b. Train a custom dataset from scratch # train efficientdet-d1 on a custom dataset # with batchsize 8 and learning rate 1e-5 python train.py -c 1 -p your_project_name --batch_size 8 --lr 1e-5 ### 3.c. Train a custom dataset with pretrained weights (Highly Recommended) # train efficientdet-d2 on a custom dataset with pretrained weights # with batchsize 8 and learning rate 1e-5 for 10 epoches python train.py -c 2 -p your_project_name --batch_size 8 --lr 1e-5 --num_epochs 10 \ --load_weights /path/to/your/weights/efficientdet-d2.pth # with a coco-pretrained, you can even freeze the backbone and train heads only # to speed up training and help convergence. python train.py -c 2 -p your_project_name --batch_size 8 --lr 1e-5 --num_epochs 10 \ --load_weights /path/to/your/weights/efficientdet-d2.pth \ --head_only True ### 4. Early stopping a training session # while training, press Ctrl+c, the program wil