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A PyTorch implementation of EfficientDet.
It is based on the
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:
timm
model collection that supports feature extraction (features_only
arg) can be used as a bacbkone.timm
0.9timm
convert_sync_batchnorm function as it handles updated models w/ BatchNormAct2d layersefficientnetv2_ds
weights 50.1 mAP @ 1024x0124, using AGC clipping and timm
's efficientnetv2_rw_s
backbone. Memory use comparable to D3, speed faster than D4. Smaller than optimal training batch size so can probably do better...efficientnetv2_dt
weights to a new set, 46.1 mAP @ 768x768, 47.0 mAP @ 896x896 using AGC clipping.timm
). Idea from (High-Performance Large-Scale Image Recognition Without Normalization
- https://arxiv.org/abs/2102.06171)timm
minimum version bumped up to 0.4.12efficientnetv2_dt
based on timm
's efficientnetv2_rw_t
(tiny) model. 45.8 mAP @ 768x768.tf_efficientdet_d?_ap
efficientdet_q1
(replace prev model at 40.6)cspresdet50
cspdarkdet53m
--torchscript
) is possible with inclusion of ModelEmaV2 from timm
and previous torchscript compat additions. Big speed gains for CPU bound training.efficientdet_q0/q1/q2
) and CSPResDeXt + PAN (cspresdext50pan
). See updated table below. Special thanks to Artus for providing resources for training the Q2 model.max
/avg
poolnew_focal
, use --legacy-focal
arg to use the original. Legacy uses less memory, but has more numerical stability issues.timm
>= 0.3.2 required, NOTE double check any custom defined model config for breaking changeMerged a few months of accumulated fixes and additions.
size % 128 = 0
on each dim.A few things on priority list I haven't tackled yet:
NOTE There are some breaking changes:
timm
to the latest (>=0.3), as some APIs for helpers changed a bit.Training sanity checks were done on VOC and OI
The table below contains models with pretrained weights. There are quite a number of other models that I have defined in model configurations that use various timm
backbones.
Variant | mAP (val2017) | mAP (test-dev2017) | mAP (TF official val2017) | mAP (TF official test-dev2017) | Params (M) | Img Size |
---|---|---|---|---|---|---|
tf_efficientdet_lite0 | 27.1 | TBD | 26.4 | N/A | 3.24 | 320 |
tf_efficientdet_lite1 | 32.2 | TBD | 31.5 | N/A | 4.25 | 384 |
efficientdet_d0 | 33.6 | TBD | N/A | N/A | 3.88 | 512 |
tf_efficientdet_d0 | 34.2 | TBD | 34.3 | 34.6 | 3.88 | 512 |
tf_efficientdet_d0_ap | 34.8 | TBD | 35.2 | 35.3 | 3.88 | 512 |
efficientdet_q0 | 35.7 | TBD | N/A | N/A | 4.13 | 512 |
tf_efficientdet_lite2 | 35.9 | TBD | 35.1 | N/A | 5.25 | 448 |
efficientdet_d1 | 39.4 | 39.5 | N/A | N/A | 6.62 | 640 |
tf_efficientdet_lite3 | 39.6 | TBD | 38.8 | N/A | 8.35 | 512 |
tf_efficientdet_d1 | 40.1 | TBD | 40.2 | 40.5 | 6.63 | 640 |
tf_efficientdet_d1_ap | 40.8 | TBD | 40.9 | 40.8 | 6.63 | 640 |
efficientdet_q1 | 40.9 | TBD | N/A | N/A | 6.98 | 640 |
cspresdext50pan | 41.2 | TBD | N/A | N/A | 22.2 | 640 |
resdet50 | 41.6 | TBD | N/A | N/A | 27.6 | 640 |
efficientdet_q2 | 43.1 | TBD | N/A | N/A | 8.81 | 768 |
cspresdet50 | 43.2 | TBD | N/A | N/A | 24.3 | 768 |
tf_efficientdet_d2 | 43.4 | TBD | 42.5 | 43 | 8.10 | 768 |
tf_efficientdet_lite3x | 43.6 | TBD | 42.6 | N/A | 9.28 | 640 |
tf_efficientdet_lite4 | 44.2 | TBD | 43.2 | N/A | 15.1 | 640 |
tf_efficientdet_d2_ap | 44.2 | TBD | 44.3 | 44.3 | 8.10 | 768 |
cspdarkdet53m | 45.2 | TBD | N/A | N/A | 35.6 | 768 |
efficientdetv2_dt | 46.1 | TBD | N/A | N/A | 13.4 | 768 |
tf_efficientdet_d3 | 47.1 | TBD | 47.2 | 47.5 | 12.0 | 896 |
tf_efficientdet_d3_ap | 47.7 | TBD | 48.0 | 47.7 | 12.0 | 896 |
tf_efficientdet_d4 | 49.2 | TBD | 49.3 | 49.7 | 20.7 | 1024 |
efficientdetv2_ds | 50.1 | TBD | N/A | N/A | 26.6 | 1024 |
tf_efficientdet_d4_ap | 50.2 | TBD | 50.4 | 50.4 | 20.7 | 1024 |
tf_efficientdet_d5 | 51.2 | TBD | 51.2 | 51.5 | 33.7 | 1280 |
tf_efficientdet_d6 | 52.0 | TBD | 52.1 | 52.6 | 51.9 | 1280 |
tf_efficientdet_d5_ap | 52.1 | TBD | 52.2 | 52.5 | 33.7 | 1280 |
tf_efficientdet_d7 | 53.1 | 53.4 | 53.4 | 53.7 | 51.9 | 1536 |
tf_efficientdet_d7x | 54.3 | TBD | 54.4 | 55.1 | 77.1 | 1536 |
See model configurations 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.
Tested in a Python 3.7 - 3.9 conda environment in Linux with:
pip install timm
or local install from (https://github.com/rwightman/pytorch-image-models)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
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 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
./distributed_train.sh 4 /mscoco --model tf_efficientdet_d0 -b 16 --amp --lr .09 --warmup-epochs 5 --sync-bn --opt fusedmomentum --model-ema
NOTE:
--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.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
Evaluate on VOC2012 validation set:
python validate.py /data/VOCdevkit --model efficientdet_d0 --num-gpu 2 --dataset voc2007 --checkpoint mychekpoint.pth --num-classes 20
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
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
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 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 {} \;
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>
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.
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:
timm
EfficientNetV2 backbones and the latest versions of the EfficientDet models 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...
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:
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
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.
NOTE: I've only tried submitting D7 to dev server for sanity check so far
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
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
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
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
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
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
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
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
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
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
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.
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EfficientDet for PyTorch
We found that effdet demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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