EfficientDet (PyTorch)

A PyTorch implementation of EfficientDet.

It is based on the

official Tensorflow implementation by Mingxing Tan and the Google Brain team
paper by Mingxing Tan, Ruoming Pang, Quoc V. Le EfficientDet: Scalable and Efficient Object Detection

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.
- Currently this is includes to all models implemented by the EficientNet and MobileNetv3 classes (which also includes MNasNet, MobileNetV2, MixNet and more). More soon...

Updates / Tasks

2020-06-04

Latest results in and training goal achieved. Slightly bested the TF model mAP results for D0 model. This model uses:

typical PyTorch symmetric padding (instead of TF compatible SAME)
my PyTorch trained EfficientNet-B0 as the pretrained starting weights (from timm)
BiFPN/Head layers without any redundant conv/BN bias layers (slightly fewer params 3877763 vs 3880067)

My latest D0 run:

 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

TF ported D0 weights:

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.335653
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.516253
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.353884
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.125278
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.386957
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.528071
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.288049
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.439918
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.466877
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.193482
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.549262
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.686037

Pretrained weights added for this model efficientdet_d0 (Tensorflow port is tf_efficientdet_d0)

2020-05-27

A D0 result in, started before last improvements: Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.331
Another D0 and D1 running with the latest code.

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

Previous D0 result: Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.324

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

Possible Future Tasks

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	efficientdet_d0.pth	33.6	TBD	33.5	33.8
D0	tf_efficientdet_d0.pth	33.6	TBD	33.5	33.8
D1	tf_efficientdet_d1.pth	39.3	TBD	39.1	39.6
D2	tf_efficientdet_d2.pth	42.6	43.1	42.5	43
D3	tf_efficientdet_d3.pth	46.0	TBD	45.9	45.8
D4	tf_efficientdet_d4.pth	49.1	TBD	49.0	49.4
D5	tf_efficientdet_d5.pth	50.4	TBD	50.5	50.7
D6	tf_efficientdet_d6.pth	51.2	TBD	51.3	51.7
D7	tf_efficientdet_d7.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 .04 --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.

Examples of Training / Fine-Tuning on Alternate Datasets

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
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 --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

Ported Tensorflow weights

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

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EfficientDet (PyTorch)

Updates / Tasks

2020-06-04

2020-05-27

2020-05-22 / 23

2020-05-04

2020-05-02

2020-04-15

2020-04-11

2020-04-10

2020-04-09

Core Tasks

Possible Future Tasks

Models

Usage

Environment Setup

Dataset Setup

Run COCO Evaluation

Run Inference

Run Training

Examples of Training / Fine-Tuning on Alternate Datasets

Results

My Training

EfficientDet-D0

Ported Tensorflow weights

TEST-DEV2017

EfficientDet-D2

EfficientDet-D7

VAL2017

EfficientDet-D0

EfficientDet-D1

EfficientDet-D2

EfficientDet-D3

EfficientDet-D4

EfficientDet-D5

EfficientDet-D6

EfficientDet-D7

简介

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