Metadata-Version: 2.1
Name: effdet
Version: 0.2.4
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)
        
        A 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) 
        
        There are other PyTorch implementations. Either their approach didn't fit my aim to correctly reproduce the Tensorflow models (but with a PyTorch feel and flexibility) or they cannot come close to replicating MS COCO training from scratch.
        
        Aside from the default model configs, there is a lot of flexibility to facilitate experiments and rapid improvements here -- some options based on the official Tensorflow impl, some of my own:
        * BiFPN connections and combination mode are fully configurable and not baked into the model code
        * BiFPN and head modules can be switched between depthwise separable or standard convolutions
        * Activations, batch norm layers are switchable via arguments (soon config)
        * Any backbone in my `timm` model collection that supports feature extraction (`features_only` arg) can be used as a bacbkone.
        
        ## Updates
        
        ### 2021-04-30
        * Add EfficientDet AdvProp-AA weights for D0-D5 from https://github.com/google/automl/blob/master/efficientdet/Det-AdvProp.md
        
        ### 2021-02-18
        * Add some new model weights with bilinear interpolation for upsample and downsample in FPN.
          * 40.9 mAP - `efficientdet_q1`  (replace prev model at 40.6)
          * 43.2 mAP -`cspresdet50`
          * 45.2 mAP - `cspdarkdet53m`
        
        ### 2020-12-07
        * Training w/ fully jit scripted model + bench (`--torchscript`) is possible with inclusion of ModelEmaV2 from `timm` and previous torchscript compat additions. Big speed gains for CPU bound training.
        * Add weights for alternate FPN layouts. QuadFPN experiments (`efficientdet_q0/q1/q2`) and CSPResDeXt + PAN (`cspresdext50pan`). See updated table below. Special thanks to [Artus](https://twitter.com/artuskg) for providing resources for training the Q2 model.
        * Heads can have a different activation from FPN via config
        * FPN resample (interpolation) can be specified via config and include any F.interpolation method or `max`/`avg` pool
        * Default focal loss changed back to `new_focal`, use `--legacy-focal` arg to use the original. Legacy uses less memory, but has more numerical stability issues.
        * custom augmentation transform and collate fn can be passed to loader factory
        * `timm` >= 0.3.2 required, NOTE double check any custom defined model config for breaking change 
        * PyTorch >= 1.6 now required
        
        ### 2020-11-12
        * add experimental PAN and Quad FPN configs to the existing EfficientDet BiFPN w/ two test model configs
        * switch untrained experimental model configs to use torchscript compat bn head layout by default
        
        ### 2020-11-09
        * set model config to read-only after creation to reduce likelyhood of misuse
        * no accessing model or bench .config attr in forward() call chain (for torcscript compat)
        * numerous smaller changes that allow jit scripting of the model or train/predict bench
        
        ### 2020-10-30
        Merged a few months of accumulated fixes and additions.
        * Proper fine-tuning compatible model init (w/ changeable # classes and proper init, demoed in train.py)
        * A new dataset interface with dataset support (via parser classes) for COCO, VOC 2007/2012, and OpenImages V5/Challenge2019
        * New focal loss def w/ label smoothing available as an option, support for jit of loss fn for (potential) speedup
        * Improved a few hot spots that squeek out a couple % of throughput gains, higher GPU utilization
        * Pascal / OpenImages evaluators based on Tensorflow Models Evaluator framework (usable for other datasets as well)
        * Support for native PyTorch DDP, SyncBN, and AMP in PyTorch >= 1.6. Still defaults to APEX if installed.
        * Non-square input image sizes are allowed for the model (the anchor layout). Specified by image_size tuple in model config. Currently still restricted to `size % 128 = 0` on each dim.
        * Allow anchor target generation to be done in either dataloader process' via collate or in model as in past. Can help balance compute.
        * Filter out unused target cls/box from dataset annotations in fixed size batch tensors before passing to target assigner. Seems to speed convergence.
        * Letterbox aware Random Erasing augmentation added.
        * A (very slow) SoftNMS impl added for inference/validation use. It can be manually enabled right now, can add arg if demand.
        * Tested with PyTorch 1.7
        * Add ResDet50 model weights, 41.6 mAP.
        
        A few things on priority list I haven't tackled yet:
        * Mosaic augmentation
        * bbox IOU loss (tried a bit but so far not a great result, need time to debug/improve)
        
        **NOTE** There are some breaking changes:
        * Predict and Train benches now output XYXY boxes, NOT XYWH as before. This was done to support other datasets as XYWH is COCO's evaluator requirement.
        * The TF Models Evaluator operates on YXYX boxes like the models. Conversion from XYXY is currently done by default. Why don't I just keep everything YXYX? Because PyTorch GPU NMS operates in XYXY.
        * You must update your version of `timm` to the latest (>=0.3), as some APIs for helpers changed a bit.
        
        Training sanity checks were done on VOC and OI
          * 80.0 @ 50 mAP finetune on voc0712 with no attempt to tune params (roughly as per command below)
          * 18.0 mAP @ 50 for OI Challenge2019 after couple days of training (only 6 epochs, eek!). It's much bigger, and takes a LOONG time, many classes are quite challenging.
          
        ### 2020-09-03
        * All models updated to latest checkpoints from TF original.
        * Add experimental soft-nms code, must be manually enabled right now. It is REALLY slow, .1-.2 mAP increase.
        
        ### 2020-07-27
        * Add updated TF ported weights for D3 model (better training) and model def and weights for new D7X model (54.3 val mAP)
        * Fix Windows bug so it at least trains in non-distributed mode
        
        ### 2020-06-15
        Add updated D7 weights from Tensorflow impl, 53.1 validation mAP here (53.4 in TF)
        
        ### 2020-06-14
        New model results, I've trained a D1 model with some WIP augmentation enhancements (not commited), just squeaking by official weights.
        
        EfficientDet-D1:
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.393798
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.586831
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.420305
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191880
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.455586
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.571316
        ```
        
        Also, [Soyeb Nagori](https://github.com/soyebn) trained an EfficientDet-Lite0 config using this code and contributed the weights.
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.319861
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.500062
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.336777
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.111257
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.378062
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.501938
        ```
        
        Unlike the other tf_ prefixed models this is not ported from (as of yet unreleased) TF official model, but it used
        TF ported weights from `timm` for the pretrained imagenet model as the backbone init, thus it uses SAME padding. 
        
        
        ## Models
        
        The table below contains models with pretrained weights. There are quite a number of other models that I have defined in [model configurations](effdet/config/model_config.py) that use various `timm` backbones.
        
        | Variant | mAP (val2017) | mAP (test-dev2017) | mAP (TF official val2017) | mAP (TF official test-dev2017) | Params (M) |
        | --- | :---: | :---: | :---: | :---: | :---: |
        | tf_efficientdet_lite0 | 32.0 | TBD | N/A | N/A | 3.24 |
        | efficientdet_d0 | 33.6 | TBD | 33.5 | 33.8 | 3.88 |
        | tf_efficientdet_d0 | 34.2 | TBD | 34.3 | 34.6 | 3.88 |
        | tf_efficientdet_d0_ap | 34.8 | TBD | 35.2 | 35.3 | 3.88 |
        | efficientdet_q0 | 35.7 | TBD | N/A | N/A | 4.13 |
        | efficientdet_d1 | 39.4 | 39.5 | 39.1 | 39.6 | 6.62 |
        | tf_efficientdet_d1 | 40.1 | TBD | 40.2 | 40.5 | 6.63 |
        | tf_efficientdet_d1_ap | 40.8 | TBD | 40.9 | 40.8 | 6.63 |
        | efficientdet_q1 | 40.9 | TBD | N/A | N/A | 6.98 |
        | cspresdext50pan | 41.2 | TBD | N/A | N/A | 22.2 |
        | resdet50 | 41.6 | TBD | N/A | N/A | 27.6 |
        | efficientdet_q2 | 43.1 | TBD | N/A | N/A | 8.81 |
        | cspresdet50 | 43.2 | TBD | N/A | N/A | 24.3 |
        | tf_efficientdet_d2 | 43.4 | TBD | 42.5 | 43 | 8.10 |
        | tf_efficientdet_d2_ap | 44.2 | TBD | 44.3 | 44.3 | 8.10 |
        | cspdarkdet53m | 45.2 | TBD | N/A | N/A | 35.6 |
        | tf_efficientdet_d3 | 47.1 | TBD | 47.2 | 47.5 | 12.0 |
        | tf_efficientdet_d3_ap | 47.7 | TBD | 48.0 | 47.7 | 12.0 |
        | tf_efficientdet_d4 | 49.2 | TBD | 49.3 | 49.7 | 20.7 |
        | tf_efficientdet_d4_ap | 50.2 | TBD | 50.4 | 50.4 | 20.7 |
        | tf_efficientdet_d5 | 51.2 | TBD | 51.2 | 51.5 | 33.7 |
        | tf_efficientdet_d6 | 52.0 | TBD | 52.1 | 52.6 | 51.9 |
        | tf_efficientdet_d5_ap | 52.1 | TBD | 52.2 | 52.5 | 33.7 |
        | tf_efficientdet_d7 | 53.1 | 53.4 | 53.4 | 53.7 | 51.9 |
        | tf_efficientdet_d7x | 54.3 | TBD | 54.4 | 55.1 | 77.1 |
        
        
        See [model configurations](effdet/config/model_config.py) for model checkpoint urls and differences.
        
        _NOTE: Official scores for all modules now using soft-nms, but still using normal NMS here._
        
        _NOTE: In training some experimental models, I've noticed some potential issues with the combination of synchronized BatchNorm (`--sync-bn`) and model EMA weight everaging (`--model-ema`) during distributed training. The result is either a model that fails to converge, or appears to converge (training loss) but the eval loss (running BN stats) is garbage. I haven't observed this with EfficientNets, but have with some backbones like CspResNeXt, VoVNet, etc. Disabling either EMA or sync bn seems to eliminate the problem and result in good models. I have not fully characterized this issue._
        
        ## Environment Setup
        
        Tested in a Python 3.7 or 3.8 conda environment in Linux with:
        * PyTorch 1.6, 1.7, 1.7.1
        * PyTorch Image Models (timm) >= 0.3.2, `pip install timm` or local install from (https://github.com/rwightman/pytorch-image-models)
        * Apex AMP master (as of 2020-08)
        
        *NOTE* - There is a conflict/bug with Numpy 1.18+ and pycocotools 2.0, force install numpy <= 1.17.5 or ensure you install pycocotools >= 2.0.2
        
        ## Dataset Setup and Use
        
        ### COCO
        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
        ```
        
        #### COCO Evaluation
        
        Run validation (val2017 by default) with D2 model: `python validate.py /localtion/of/mscoco/ --model tf_efficientdet_d2`
        
        
        Run test-dev2017: `python validate.py /localtion/of/mscoco/ --model tf_efficientdet_d2 --split testdev`
        
        #### COCO Training
        
        `./distributed_train.sh 4 /mscoco --model tf_efficientdet_d0 -b 16 --amp  --lr .09 --warmup-epochs 5  --sync-bn --opt fusedmomentum --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.
        
        
        ### Pascal VOC
        
        2007, 2012, and combined 2007 + 2012 w/ labeled 2007 test for validation are supported.
        
        ```
        wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
        wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
        wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
        find . -name '*.tar' -exec tar xf {} \;
        ```
        
        There should be a `VOC2007` and `VOC2012` folder within `VOCdevkit`, dataset root for cmd line will be VOCdevkit.
        
        Alternative download links, slower but up more often than ox.ac.uk:
        ```
        http://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
        http://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
        http://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
        ```
        
        #### VOC Evaluation
        
        Evaluate on VOC2012 validation set:
        `python validate.py /data/VOCdevkit --model efficientdet_d0 --num-gpu 2 --dataset voc2007 --checkpoint mychekpoint.pth --num-classes 20`
        
        #### VOC Training
        
        Fine tune COCO pretrained weights to VOC 2007 + 2012:
        `/distributed_train.sh 4 /data/VOCdevkit --model efficientdet_d0 --dataset voc0712 -b 16 --amp --lr .008 --sync-bn --opt fusedmomentum --warmup-epochs 3 --model-ema --model-ema-decay 0.9966 --epochs 150 --num-classes 20 --pretrained`
        
        ### OpenImages
        
        Setting up OpenImages dataset is a commitment. I've tried to make it a bit easier wrt to the annotations, but grabbing the dataset is still going to take some time. It will take approx 560GB of storage space.
        
        To download the image data, I prefer the CVDF packaging. The main OpenImages dataset page, annotations, dataset license info can be found at: https://storage.googleapis.com/openimages/web/index.html
        
        #### CVDF Images Download
        
        Follow the s3 download directions here: https://github.com/cvdfoundation/open-images-dataset#download-images-with-bounding-boxes-annotations
        
        Each `train_<x>.tar.gz` should be extracted to `train/<x>` folder, where x is a hex digit from 0-F. `validation.tar.gz` can be extracted as flat files into `validation/`.
        
        #### Annotations Download
        
        Annotations can be downloaded separately from the OpenImages home page above. For convenience, I've packaged them all together with some additional 'info' csv files that contain ids and stats for all image files. My datasets rely on the `<set>-info.csv` files. Please see https://storage.googleapis.com/openimages/web/factsfigures.html for the License of these annotations. The annotations are licensed by Google LLC under CC BY 4.0 license. The images are listed as having a CC BY 2.0 license.
        ```
        wget https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1-anno/openimages-annotations.tar.bz2
        wget https://github.com/rwightman/efficientdet-pytorch/releases/download/v0.1-anno/openimages-annotations-challenge-2019.tar.bz2
        find . -name '*.tar.bz2' -exec tar xf {} \;
        ```
        
        #### Layout
        
        Once everything is downloaded and extracted the root of your openimages data folder should contain:
        ```
        annotations/<csv anno for openimages v5/v6>
        annotations/challenge-2019/<csv anno for challenge2019>
        train/0/<all the image files starting with '0'>
        .
        .
        .
        train/f/<all the image files starting with 'f'>
        validation/<all the image files in same folder>
        ```
        
        #### OpenImages Training
        Training with Challenge2019 annotations (500 classes):
        `./distributed_train.sh 4 /data/openimages --model efficientdet_d0 --dataset openimages-challenge2019 -b 7 --amp --lr .042 --sync-bn --opt fusedmomentum --warmup-epochs 1 --lr-noise 0.4 0.9 --model-ema --model-ema-decay 0.999966 --epochs 100 --remode pixel --reprob 0.15 --recount 4 --num-classes 500 --val-skip 2`
        
        The 500 (Challenge2019) or 601 (V5/V6) class head for OI takes up a LOT more GPU memory vs COCO. You'll likely need to half batch sizes.
        
        ### Examples of Training / Fine-Tuning on Custom Datasets
        
        The models here have been used with custom training routines and datasets with great results. There are lots of details to figure out so please don't file any 'I get crap results on my custom dataset issues'. If you can illustrate a reproducible problem on a public, non-proprietary, downloadable dataset, with public github fork of this repo including working dataset/parser implementations, I MAY have time to take a look.
        
        Examples:
        * Alex Shonenkov has a clear and concise Kaggle kernel which illustrates fine-tuning these models for detecting wheat heads: https://www.kaggle.com/shonenkov/training-efficientdet (this is out of date wrt to latest versions here)
        
        If you have a good example script or kernel training these models with a different dataset, feel free to notify me for inclusion here...
        
        ## Results
        
        ### My Training
        
        #### EfficientDet-D0
        
        Latest training run with .336 for D0 (on 4x 1080ti):
        `./distributed_train.sh 4 /mscoco --model efficientdet_d0 -b 22 --amp --lr .12 --sync-bn --opt fusedmomentum --warmup-epochs 5 --lr-noise 0.4 0.9 --model-ema --model-ema-decay 0.9999`
        
        These hparams above resulted in a good model, a few points:
        * the mAP peaked very early (epoch 200 of 300) and then appeared to overfit, so likely still room for improvement
        * I enabled my experimental LR noise which tends to work well with EMA enabled
        * the effective LR is a bit higher than official. Official is .08 for batch 64, this works out to .0872
        * drop_path (aka survival_prob / drop_connect) rate of 0.1, which is higher than the suggested 0.0 for D0 in official, but lower than the 0.2 for the other models
        * longer EMA period than default
        
        VAL2017
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.336251
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.521584
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.356439
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.123988
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.395033
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.521695
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.287121
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.441450
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.467914
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.197697
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.552515
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.689297
        ```
        
        #### EfficientDet-D1 
        
        Latest run with .394 mAP (on 4x 1080ti):
        `./distributed_train.sh 4 /mscoco --model efficientdet_d1 -b 10 --amp --lr .06 --sync-bn --opt fusedmomentum --warmup-epochs 5 --lr-noise 0.4 0.9 --model-ema --model-ema-decay 0.99995`
        
        For this run I used some improved augmentations, still experimenting so not ready for release, should work well without them but will likely start overfitting a bit sooner and possibly end up a in the .385-.39 range.
        
        
        ### Ported Tensorflow weights
        
        #### TEST-DEV2017
        
        NOTE: I've only tried submitting D7 to dev server for sanity check so far
        
        ##### TF-EfficientDet-D7
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.534
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.726
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.577
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.356
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.569
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.660
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.397
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.644
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.682
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.508
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.718
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.818
         ```
        
        #### VAL2017
        
        ##### TF-EfficientDet-D0
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.341877
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.525112
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.360218
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.131366
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.399686
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.537368
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.293137
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.447829
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.472954
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.195282
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.558127
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.695312
        ```
        
        ##### TF-EfficientDet-D1
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.401070
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.590625
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.422998
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211116
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.459650
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577114
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.326565
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.507095
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.537278
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.308963
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.610450
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.731814
        ```
        
        ##### TF-EfficientDet-D2
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.434042
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.627834
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.463488
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237414
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.486118
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.606151
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.343016
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.538328
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.571489
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.350301
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.638884
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.746671
        ```
        
        ##### TF EfficientDet-D3
        
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.471223
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.661550
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.505127
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.301385
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.518339
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.626571
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.365186
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.582691
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.617252
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.424689
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.670761
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.779611
        ```
        
        ##### TF-EfficientDet-D4
         ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.491759
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.686005
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.527791
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.325658
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.536508
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.635309
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.373752
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.601733
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.638343
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.463057
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.685103
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.789180
        ```
        
        ##### TF-EfficientDet-D5
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.511767
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.704835
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.552920
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.355680
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551341
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.650184
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.384516
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.619196
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.657445
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.499319
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.695617
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.788889
        ```
        
        ##### TF-EfficientDet-D6
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.520200
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.713204
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.560973
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.361596
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.567414
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.657173
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.387733
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.629269
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.667495
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.499002
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.711909
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.802336
        ```
        
        ##### TF-EfficientDet-D7
         ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.531256
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.724700
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.571787
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.368872
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.573938
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.668253
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.393620
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.637601
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.676987
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.524850
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.717553
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.806352
         ```
        
        ##### TF-EfficientDet-D7X
        
        ```
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.543
         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.737
         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.585
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.401
         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.579
         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.680
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.398
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.649
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.689
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.550
         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.725
         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.823
        ```
        
        ## TODO
        - [x] Basic Training (object detection) reimplementation
        - [ ] Mosaic Augmentation
        - [ ] Rand/AutoAugment
        - [ ] BBOX IoU loss (giou, diou, ciou, 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
        - [x] 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.
        
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
