Metadata-Version: 2.1
Name: effdet
Version: 0.1.2
Summary: EfficientDet for PyTorch
Home-page: https://github.com/rwightman/efficientdet-pytorch
Author: Ross Wightman
Author-email: hello@rwightman.com
License: UNKNOWN
Description: # EfficientDet (PyTorch)
        
        This is a work in progress PyTorch implementation of EfficientDet. 
        
        It is based on the
        * official Tensorflow implementation by [Mingxing Tan and the Google Brain team](https://github.com/google/automl)
        * paper by Mingxing Tan, Ruoming Pang, Quoc V. Le [EfficientDet: Scalable and Efficient Object Detection](https://arxiv.org/abs/1911.09070) 
        
        I am aware there are other PyTorch implementations. Their approach didn't fit well with my aim to replicate the Tensorflow models closely enough to allow weight ports while still maintaining a PyTorch feel and a high degree of flexibility for future additions. So, this is built from scratch and leverages my previous EfficientNet work.
        
        ## Updates / Tasks
        
        ### 2020-05-22 / 23
        A bunch of changes:
        * COCO eval per epoch for better selection of checkpoints while training, works with distributed
        * optimizations to both train and inference that should see small throughput gains
        * doing the above, attempted to torchscript the full training loss + anchor labeler but ran into problems so had to back out part way due messy hacks or weird AMP issues causing silent bad results. Hopefully in PyTorch 1.6 there will be less TS issues.
        * updated results after clipping boxes, now pretty much exact match to official, even slightly better on a few models
        * added model factory, pretrained download, cleanup model configs
        * setup.py, pypi release
        
        ### 2020-05-04
        Initial D1 training results in -- close but not quite there. Definitely in reach and better than any other non-official EfficientDet impl I've seen.
        
        Biggest missing element is proper per-epoch mAP validation for better checkpoint selection (than loss based). I was resisting doing full COCO eval because it's so slow, but may throw that in for now...
        
        D1:
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.382
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.577
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.407
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.437
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.552
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.314
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.489
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.520
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.286
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.591
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.713
        ```
        
        Previous D0 result:
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.324
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.513
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.342
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.121
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.383
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.499
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.280
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.426
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.452
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.188
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.532
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.668
        ```
        
        ### 2020-05-02
        First decent MSCOCO training results (from scratch, w/ pretrained classification backbone weights as starting point). 32.4 mAP for D0. Working on improvements and D1 trials still running.
        
        ### 2020-04-15
        Taking a pause on training, some high priority things came up. There are signs of life on the training branch, was working the basic augs before priority switch, loss fn appeared to be doing something sane with distributed training working, no proper eval yet, init not correct yet. I will get to it, with SOTA training config and good performance as the end goal (as with my EfficientNet work). 
        
        ### 2020-04-11
        Cleanup post-processing. Less code and a five-fold throughput increase on the smaller models. D0 running > 130 img/s on a single 2080Ti, D1 > 130 img/s on dual 2080Ti up to D7 @ 8.5 img/s.
        
        ### 2020-04-10
        Replace `generate_detections` with PyTorch impl using torchvision batched_nms. Significant performance increase with minor (+/-.001 mAP) score differences. Quite a bit faster than original TF impl on a GPU now.
        
        ### 2020-04-09
        Initial code with working validation posted. Yes, it's a little slow, but I think faster than the official impl on a GPU if you leave AMP enabled. Post processing needs some love. 
        
        ### Core Tasks
        - [x] Feature extraction from my EfficientNet implementations (https://github.com/rwightman/gen-efficientnet-pytorch or https://github.com/rwightman/pytorch-image-models)
        - [x] Low level blocks / helpers (SeparableConv, create_pool2d (same padding), etc)
        - [x] PyTorch implementation of BiFPN, BoxNet, ClassNet modules and related submodules
        - [x] Port Tensorflow checkpoints to PyTorch -- initial D1 checkpoint converted, state_dict loaded, on to validation....
        - [x] Basic MS COCO validation script
          - [x] Temporary (hacky) COCO dataset and transform 
          - [x] Port reference TF anchor and object detection code
          - [x] Verify model output sanity
          - [X] Integrate MSCOCO eval metric calcs
        - [x] Some cleanup, testing
        - [x] Submit to test-dev server, all good
        - [ ] Add torch hub support and pretrained URL based weight download
        - [x] Remove redundant bias layers that exist in the official impl and weights
        - [ ] Add visualization support
        - [x] Performance improvements, numpy TF detection code -> optimized PyTorch
        - [ ] Verify/fix Torchscript and ONNX export compatibility
        - [ ] Try PyTorch 1.5 w/ NHWC (channels last) order which matches TF impl
        
        ### Possible Future Tasks
        - [x] Basic Training (object detection) reimplementation
        - [ ] Advanced Training w/ Rand/AutoAugment, etc
        - [ ] Training (semantic segmentation) experiments
        - [ ] Integration with Detectron2 / MMDetection codebases
        - [ ] Addition and cleanup of EfficientNet based U-Net and DeepLab segmentation models that I've used in past projects
        - [ ] Addition and cleanup of OpenImages dataset/training support from a past project
        - [ ] Exploration of instance segmentation possibilities...
        
        If you are an organization is interested in sponsoring and any of this work, or prioritization of the possible future directions interests you, feel free to contact me (issue, LinkedIn, Twitter, hello at rwightman dot com). I will setup a github sponser if there is any interest.
        
        ## Models
        
        | Variant | Download | mAP (val2017) | mAP (test-dev2017) | mAP (TF official val2017) | mAP (TF official test-dev2017) |
        | --- | --- | :---: | :---: | :---: | :---: |
        | D0 | [tf_efficientdet_d0.pth](https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d0-d92fd44f.pth) | 33.6 | TBD | 33.5 | 33.8 |
        | D1 | [tf_efficientdet_d1.pth](https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d1-4c7ebaf2.pth) | 39.3 | TBD | 39.1 | 39.6 |
        | D2 | [tf_efficientdet_d2.pth](https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d2-cb4ce77d.pth) | 42.6 | 43.1 | 42.5 | 43 |
        | D3 | [tf_efficientdet_d3.pth](https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d3-b0ea2cbc.pth) | 46.0 | TBD | 45.9 | 45.8 |
        | D4 | [tf_efficientdet_d4.pth](https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d4-5b370b7a.pth) | 49.1 | TBD | 49.0 | 49.4 |
        | D5 | [tf_efficientdet_d5.pth](https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d5-ef44aea8.pth) | 50.4 | TBD | 50.5 | 50.7 |
        | D6 | [tf_efficientdet_d6.pth](https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d6-51cb0132.pth) | 51.2 | TBD | 51.3 | 51.7 |
        | D7 | [tf_efficientdet_d7.pth](https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1/tf_efficientdet_d7-f05bf714.pth) | 51.8 | 52.1 | 52.1 | 52.2 |
        
        
        ## Usage
        
        ### Environment Setup
        
        Tested in a Python 3.7 or 3.8 conda environment in Linux with:
        * PyTorch 1.4
        * PyTorch Image Models (timm) 0.1.20, `pip install timm` or local install from (https://github.com/rwightman/pytorch-image-models) 
        * Apex AMP master (as of 2020-04)
        
        *NOTE* - There is a conflict/bug with Numpy 1.18+ and pycocotools, force install numpy <= 1.17.5 or the coco eval will fail,
        the validation script will still save the output JSON and that can be run through eval again later. 
        
        ### Dataset Setup
        
        MSCOCO 2017 validation data:
        ```
        wget http://images.cocodataset.org/zips/val2017.zip
        wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
        unzip val2017.zip
        unzip annotations_trainval2017.zip
        ```
        
        MSCOCO 2017 test-dev data:
        ```
        wget http://images.cocodataset.org/zips/test2017.zip
        unzip -q test2017.zip
        wget http://images.cocodataset.org/annotations/image_info_test2017.zip
        unzip image_info_test2017.zip
        ```
        
        ### Run COCO Evaluation
        
        Run validation (val2017 by default) with D2 model: `python validation.py /localtion/of/mscoco/ --model tf_efficientdet_d2 --checkpoint tf_efficientdet_d2.pth`
        
        Run test-dev2017: `python validation.py /localtion/of/mscoco/ --model tf_efficientdet_d2 --checkpoint tf_efficientdet_d2.pth --anno test-dev2017`
        
        ### Run Inference
        
        TODO: Need an inference script
        
        ### Run Training
        
        `./distributed_train.sh 2 /mscoco --model tf_efficientdet_d0 -b 16 --amp  --lr .05 --warmup-epochs 5  --sync-bn --opt fusedmomentum --fill-color mean --model-ema`
        
        NOTE:
        * Training script currently defaults to a model that does NOT have redundant conv + BN bias layers like the official models, set correct flag when validating.
        * I've only trained with img mean (`--fill-color mean`) as the background for crop/scale/aspect fill, the official repo uses black pixel (0) (`--fill-color 0`). Both likely work fine.
        * The official training code uses EMA weight averaging by default, it's not clear there is a point in doing this with the cosine LR schedule, I find the non-EMA weights end up better than EMA in the last 10-20% of training epochs 
        * The default h-params is a very close to unstable (exploding loss), don't try using Nesterov momentum. Try to keep the batch size up, use sync-bn.
        
        ## Results
        
        ### TEST-DEV2017
        
        NOTE: I've only tried submitting D2 and D7 to dev server for sanity check so far
        
        #### EfficientDet-D2
        
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.431
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.624
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.463
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.226
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.471
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.585
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.345
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.543
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.575
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.342
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.632
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.756
        ```
        
        #### EfficientDet-D7
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.521
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.714
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.563
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.345
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.555
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.646
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.390
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.631
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.670
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.497
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.704
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.808
         ```
        
        ### VAL2017
        
        #### EfficientDet-D0
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.336
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.516
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.354
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.125
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.387
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.528
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.288
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.440
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.467
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.194
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.549
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.686
        ```
        
        #### EfficientDet-D1
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.393
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.583
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.419
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.187
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.447
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.572
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.323
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.501
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.532
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.295
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.599
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.734
        ```
        
        #### EfficientDet-D2
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.426
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.618
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.452
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.481
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.590
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.342
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.537
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.569
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.348
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.633
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.748
        ```
        
        #### EfficientDet-D3
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.460
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.651
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.493
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.283
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.503
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.360
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.570
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.605
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.409
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.655
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.768
        ```
        
        #### EfficientDet-D4
         ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.491
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.685
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.531
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.334
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.539
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.641
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.375
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.598
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.635
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.468
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.683
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.780
        ```
        
        #### EfficientDet-D5
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.700
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.543
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.337
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.549
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.646
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.381
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.617
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.654
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.485
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.696
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.791
        ```
        
        #### EfficientDet-D6
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.512
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.706
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.551
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.348
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.555
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.654
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.386
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.623
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.661
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.500
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.701
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.794
        ```
        
        #### EfficientDet-D7
         ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.518
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.711
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.558
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.368
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.564
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.655
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.386
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.627
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.665
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.505
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.704
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.801
         ```
        
Keywords: pytorch pretrained efficientdet efficientnet bifpn object detection
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6
Description-Content-Type: text/markdown
