efficientdet

Keras EfficientDet

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Summary

• Keras implementation of google/automl/efficientdet. Model weights converted from official publication. It's the h5 ones for EfficientDetD* models, not ckpt ones, as their accuracy higher.
• Paper 1911.09070 EfficientDet: Scalable and Efficient Object Detection.
• Det-AdvProp + AutoAugment Paper 2103.13886 Robust and Accurate Object Detection via Adversarial Learning.

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Models

Model	Params	FLOPs	Input	COCO val AP	test AP	Download
EfficientDetD0	3.9M	2.55G	512	34.3	34.6	efficientdet_d0.h5
- Det-AdvProp	3.9M	2.55G	512	35.1	35.3
EfficientDetD1	6.6M	6.13G	640	40.2	40.5	efficientdet_d1.h5
- Det-AdvProp	6.6M	6.13G	640	40.8	40.9
EfficientDetD2	8.1M	11.03G	768	43.5	43.9	efficientdet_d2.h5
- Det-AdvProp	8.1M	11.03G	768	44.3	44.3
EfficientDetD3	12.0M	24.95G	896	46.8	47.2	efficientdet_d3.h5
- Det-AdvProp	12.0M	24.95G	896	47.7	48.0
EfficientDetD4	20.7M	55.29G	1024	49.3	49.7	efficientdet_d4.h5
- Det-AdvProp	20.7M	55.29G	1024	50.4	50.4
EfficientDetD5	33.7M	135.62G	1280	51.2	51.5	efficientdet_d5.h5
- Det-AdvProp	33.7M	135.62G	1280	52.2	52.5
EfficientDetD6	51.9M	225.93G	1280	52.1	52.6	efficientdet_d6.h5
EfficientDetD7	51.9M	325.34G	1536	53.4	53.7	efficientdet_d7.h5
EfficientDetD7X	77.0M	410.87G	1536	54.4	55.1	efficientdet_d7x.h5
EfficientDetLite0	3.2M	0.98G	320	27.5	26.41	efficientdet_lite0.h5
EfficientDetLite1	4.2M	1.97G	384	32.6	31.50	efficientdet_lite1.h5
EfficientDetLite2	5.3M	3.38G	448	36.2	35.06	efficientdet_lite2.h5
EfficientDetLite3	8.4M	7.50G	512	39.9	38.77	efficientdet_lite3.h5
EfficientDetLite3X	9.3M	14.01G	640	44.0	42.64	efficientdet_lite3x.h5
EfficientDetLite4	15.1M	20.20G	640	44.4	43.18	efficientdet_lite4.h5

COCO val evaluation. More usage info can be found in COCO Evaluation.

# EfficientDet using resize method bilinear w/o antialias
CUDA_VISIBLE_DEVICES='0' ./coco_eval_script.py -m efficientdet.EfficientDetD0 --resize_method bilinear --disable_antialias
# >>>> [COCOEvalCallback] input_shape: (512, 512), pyramid_levels: [3, 7], anchors_mode: efficientdet

# Specify anchor_scale if not 4
CUDA_VISIBLE_DEVICES='0' ./coco_eval_script.py -m efficientdet.EfficientDetLite0 --anchor_scale 3 --resize_method bilinear --disable_antialias
# >>>> model_kwargs: {'anchor_scale': 3}
# >>>> [COCOEvalCallback] input_shape: (320, 320), pyramid_levels: [3, 7], anchors_mode: efficientdet

Usage

• Basic usage

  from keras_cv_attention_models import efficientdet
  model = efficientdet.EfficientDetD0(pretrained="coco")
  
  # Run prediction
  from keras_cv_attention_models import test_images
  imm = test_images.dog_cat()
  preds = model(model.preprocess_input(imm))
  bboxs, lables, confidences = model.decode_predictions(preds)[0]
  
  # Show result
  from keras_cv_attention_models.coco import data
  data.show_image_with_bboxes(imm, bboxs, lables, confidences, num_classes=90)

  
• Use dynamic input resolution by set input_shape=(None, None, 3).

  from keras_cv_attention_models import efficientdet
  model = efficientdet.EfficientDetD1(input_shape=(None, None, 3), pretrained="coco")
  # >>>> Load pretrained from: ~/.keras/models/efficientdet_d1_640_coco.h5
  print(model.input_shape, model.output_shape)
  # (None, None, None, 3) (None, None, 94)
  print(model(tf.ones([1, 768, 768, 3])).shape)
  # (1, 110484, 94)
  print(model(tf.ones([1, 176, 228, 3])).shape)
  # (1, 7803, 94)
  
  from keras_cv_attention_models import test_images
  imm = test_images.dog_cat()
  input_shape = (376, 227, 3)
  preds = model(model.preprocess_input(imm, input_shape=input_shape))
  bboxs, lables, confidences = model.decode_predictions(preds, input_shape=input_shape)[0]
  
  # Show result
  from keras_cv_attention_models.coco import data
  data.show_image_with_bboxes(imm, bboxs, lables, confidences, num_classes=90)

  

Custom detector using efficientdet header

• Backbone for EfficientDet can be any model with pyramid stage structure.

  from keras_cv_attention_models import efficientdet, coatnet
  bb = coatnet.CoAtNet0(input_shape=(256, 256, 3), num_classes=0)
  mm = efficientdet.EfficientDet(backbone=bb, num_classes=80)
  # >>>> features: {'stack_2_block_3_output': (None, 32, 32, 192),
  #                 'stack_3_block_5_ffn_output': (None, 16, 16, 384),
  #                 'stack_4_block_2_ffn_output': (None, 8, 8, 768)}
  
  mm.summary()  # Trainable params: 23,487,463
  print(mm.output_shape)
  # (None, 12276, 84)

  Each EfficientDetD* / EfficientDetLite* can also set with backbone=xxx for using their presets.

  from keras_cv_attention_models import efficientdet, resnest
  backbone = resnest.ResNest50(input_shape=(384, 384, 3), num_classes=0)
  mm = efficientdet.EfficientDetD2(backbone=backbone, pretrained=None, num_classes=80)
  
  mm.summary()  # Trainable params: 27,142,867

• Currently 4 types anchors supported, parameter anchors_mode controls which anchor to use, value in ["efficientdet", "anchor_free", "yolor", "yolov8"]. Default is "efficientdet".

  from keras_cv_attention_models import efficientdet, coatnet
  bb = coatnet.CoAtNet0(input_shape=(256, 256, 3), num_classes=0)
  
  mm = efficientdet.EfficientDet(backbone=bb, anchors_mode="anchor_free", num_classes=80) # Trainable params: 23,444,424
  print(mm.output_shape) # (None, 1364, 85)
  
  mm = efficientdet.EfficientDet(backbone=bb, anchors_mode="yolor", num_classes=80) # Trainable params: 23,455,474
  print(mm.output_shape) # (None, 4096, 85)

  Default settings for anchors_mode

  anchors_mode	use_object_scores	num_anchors	anchor_scale	aspect_ratios	num_scales	grid_zero_start
  efficientdet	False	9	4	[1, 2, 0.5]	3	False
  anchor_free	True	1	1	[1]	1	True
  yolor	True	3	None	presets	None	offset=0.5
  yolov8	False	1	1	[1]	1	False

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