EfficientNet | Implementation ? #3380

dexception · 2019-06-11T06:30:42Z

https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
https://ai.googleblog.com/2019/05/efficientnet-improving-accuracy-and.html
https://www.youtube.com/watch?v=3svIm5UC94I

This is good.

nseidl · 2019-06-12T02:57:40Z

+1

AlexeyAB · 2019-06-12T13:03:09Z

Paper: https://arxiv.org/abs/1905.11946v2

Classifier

EfficientNet B0 (224x224) 0.9 BFLOPS - 0.45 B_FMA (16ms / RTX 2070), 4.9M params: efficientnet_b0.cfg.txt - Training 2.5 days
71.3% Top1 - 90.4% Top5 - accuracy weights file: https://drive.google.com/open?id=1nGdWz76A2EpNhWIfDeAI3hribboilux-

While (Official) EfficientNetB0 (224x224) 0.78 BFLOPS - 0.39 FMA, 5.3M params - that is trained by official code https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet with batch size equals to 256 has lower accuracy: 70.0% Top1 and 88.9% Top5

Detector - 3.7 BFLOPs, 45.0 mAP@0.5 on COCO test-dev.

cfg-file: enet-coco.cfg.txt
weights file: https://drive.google.com/open?id=1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m

efficientnet-lite3-leaky.cfg: top-1 73.0%, top-5 92.4%. - change relu6 to leaky: activation=leaky https://github.com/AlexeyAB/darknet/blob/master/cfg/efficientnet-lite3.cfg

Classifiers: - Can be trained on ImageNet(ILSVRC2012) by using 4 x GPU 2080 TI:

EfficientNet B0 XNOR (224x224) 0.8 BFLOPS + 25 BOPS (18ms / RTX 2070): efficientnet_b0_xnor.cfg.txt - 5 days
EfficientNet B3 (288x288) 3.5 BFLOPS - 1.8 B_FMA (28ms/RTX 2070): efficientnet_b3.cfg.txt - 11 days
EfficientNet B3 (320x320) 4.3 BFLOPS - 2.2 B_FMA (30ms/RTX 2070): efficientnet_b3_320.cfg.txt - 14 days
EfficientNet B4 (384x384) 10.2 BFLOPS - 5.1 B_FMA (46ms/RTX 2070): efficientnet_b4.cfg.txt - 26 days

Training command:
./darknet classifier train cfg/imagenet1k_c.data cfg/efficientnet_b0.cfg -topk

Continue training:
./darknet classifier train cfg/imagenet1k_c.data cfg/efficientnet_b0.cfg backup/efficientnet_b0_last.weights -topk

Content of imagenet1k_c.data:

classes=1000
train  = data/imagenet1k.train_c.list
valid  = data/inet.val_c.list
backup = backup
labels = data/imagenet.labels.list
names  = data/imagenet.shortnames.list
top=5

Dataset - each image in imagenet1k.train_c.list and inet.val_c.list has one of 1000 labels from imagenet.labels.list, for example n01440764

imagenet.labels.list: https://github.com/AlexeyAB/darknet/blob/master/data/imagenet.labels.list
imagenet.shortnames.list: https://github.com/AlexeyAB/darknet/blob/master/data/imagenet.shortnames.list
ILSVRC2012 training dataset - annotated images - 138 GB: https://github.com/AlexeyAB/darknet/blob/master/scripts/get_imagenet_train.sh
ILSVRC2012 validation dataset:
- images - 6.3 GB: http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_img_val.tar
- annotations - 2.2 MB: http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_bbox_val_v3.tgz
  Set validation labels: https://github.com/AlexeyAB/darknet/blob/master/scripts/imagenet_label.sh
  read: https://pjreddie.com/darknet/imagenet/

More: http://www.image-net.org/challenges/LSVRC/2012/nonpub-downloads

Models: https://github.com/tensorflow/tpu/blob/05f7b15cdf0ae36bac84beb4aef0a09983ce8f66/models/official/efficientnet/efficientnet_builder.py#L28-L39

      # (width_coefficient, depth_coefficient, resolution, dropout_rate)
      'efficientnet-b0': (1.0, 1.0, 224, 0.2),
      'efficientnet-b1': (1.0, 1.1, 240, 0.2),
      'efficientnet-b2': (1.1, 1.2, 260, 0.3),
      'efficientnet-b3': (1.2, 1.4, 300, 0.3),
      'efficientnet-b4': (1.4, 1.8, 380, 0.4),
      'efficientnet-b5': (1.6, 2.2, 456, 0.4),
      'efficientnet-b6': (1.8, 2.6, 528, 0.5),
      'efficientnet-b7': (2.0, 3.1, 600, 0.5),

CLICK ME - EfficientNet B0 model details

#alpha=1.2, beta=1.1, gamma=1.15
#d=pow(alpha, fi), w=pow(beta, fi), r=pow(gamma, fi)
#fi=0, d=1.0, w=1.0, r=1.0 - theoretically
# in practice: https://github.com/tensorflow/tpu/blob/05f7b15cdf0ae36bac84beb4aef0a09983ce8f66/models/official/efficientnet/efficientnet_builder.py#L28-L40
# 'efficientnet-b0': (1.0, 1.0, 224, 0.2):
#  width=1.0,  depth=1.0, resolution=224,  dropout=0.2

BLOCKS 1 - 7:
      'r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25',
      'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25',
      'r3_k5_s11_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25',
      'r1_k3_s11_e6_i192_o320_se0.25',

In details: https://github.com/tensorflow/tpu/blob/05f7b15cdf0ae36bac84beb4aef0a09983ce8f66/models/official/efficientnet/efficientnet_builder.py#L61-L69

BLOCK-1

# r1_k3_s11_e1_i32_o16_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 3
#        num_repeat=int(options['r']),				num_repeat = 1
#        input_filters=int(options['i']),			input_filters = 32
#        output_filters=int(options['o']),			output_filters = 16
#        expand_ratio=int(options['e']),			expand_ratio = 1
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 1,1


BLOCK-2

# r2_k3_s22_e6_i16_o24_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 3
#        num_repeat=int(options['r']),				num_repeat = 2
#        input_filters=int(options['i']),			input_filters = 16
#        output_filters=int(options['o']),			output_filters = 24
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 2,2



BLOCK-3

# r2_k5_s22_e6_i24_o40_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 5
#        num_repeat=int(options['r']),				num_repeat = 2
#        input_filters=int(options['i']),			input_filters = 24
#        output_filters=int(options['o']),			output_filters = 40
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 2,2



BLOCK-4

# r3_k3_s22_e6_i40_o80_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 3
#        num_repeat=int(options['r']),				num_repeat = 3
#        input_filters=int(options['i']),			input_filters = 40
#        output_filters=int(options['o']),			output_filters = 80
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 2,2



BLOCK-5

# r3_k5_s11_e6_i80_o112_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 5
#        num_repeat=int(options['r']),				num_repeat = 3
#        input_filters=int(options['i']),			input_filters = 80
#        output_filters=int(options['o']),			output_filters = 112
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 1,1



BLOCK-6

# r4_k5_s22_e6_i112_o192_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 5
#        num_repeat=int(options['r']),				num_repeat = 4
#        input_filters=int(options['i']),			input_filters = 112
#        output_filters=int(options['o']),			output_filters = 192
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 2,2



BLOCK-7

# r1_k3_s11_e6_i192_o320_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 3
#        num_repeat=int(options['r']),				num_repeat = 1
#        input_filters=int(options['i']),			input_filters = 192
#        output_filters=int(options['o']),			output_filters = 320
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 1,1

CLICK ME - EfficientNet B3 model details

#alpha=1.2, beta=1.1, gamma=1.15
#d=pow(alpha, fi), w=pow(beta, fi), r=pow(gamma, fi)
#fi=3, d=1.73, w=1.33, r=1.52 - theoretically
# in practice: https://github.com/tensorflow/tpu/blob/05f7b15cdf0ae36bac84beb4aef0a09983ce8f66/models/official/efficientnet/efficientnet_builder.py#L28-L40
# 'efficientnet-b3': (1.2, 1.4, 300, 0.3):
#  width=1.2,  depth=1.4, resolution=300 (320),  dropout=0.3

# https://github.com/tensorflow/tpu/blob/05f7b15cdf0ae36bac84beb4aef0a09983ce8f66/models/official/efficientnet/efficientnet_model.py#L120-L125
# repeats_new = int(math.ceil(depth * repeats)) ### ceil - Rounds x upward,

# https://github.com/tensorflow/tpu/blob/05f7b15cdf0ae36bac84beb4aef0a09983ce8f66/models/official/efficientnet/efficientnet_builder.py#L134-L137
#      width_coefficient=width_coefficient,
#      depth_coefficient=depth_coefficient,
#      depth_divisor=8,
#      min_depth=None)
#
# https://github.com/tensorflow/tpu/blob/05f7b15cdf0ae36bac84beb4aef0a09983ce8f66/models/official/efficientnet/efficientnet_model.py#L101-L117
# multiplier = width_coefficient = 1.2
# divisor = 8
# min_depth = none
# min_depth = divisor = 8

filters = filters * 1.2
new_filters = max(8, (int(filters + 4) // 8) * 8)   ## //===floor in this case
if new_filters < 0.9 * filters:  new_filters += 8

16 *1.2=19,2
new_filters = max(8, int(19,2+4)//8  * 8) = 16 (>=16)

24 *1.2=28,8
new_filters = max(8, int(28,8+4)//8  * 8) = 32 (>24)

32 *1.2=38,4
new_filters = max(8, int(38,4+4)//8  * 8) = 40 (>32)

40 *1.2=48
new_filters = max(8, int(48+4)//8  * 8) = 48 (>40)

80 *1.2=96
new_filters = max(8, int(96+4)//8  * 8) = 96 (>80)

112 *1.2=134,4
new_filters = max(8, int(134,4+4)//8  * 8) = 136 (>112)

192 *1.2=230,4
new_filters = max(8, int(230,4+4)//8  * 8) = 232 (>192)

320 *1.2=384
new_filters = max(8, int(384+4)//8  * 8) = 384 (>320)



8 *1.2=9,6
new_filters = max(8, int(9,6+4)//8  * 8) = 8 (==8)

64 *1.2=76,8
new_filters = max(8, int(76,8+4)//8  * 8) = 80 (>64)

96 *1.2=115,2
new_filters = max(8, int(115,2+4)//8  * 8) = 112 (>96)


144 *1.2=172,8
new_filters = max(8, int(172,8+4)//8  * 8) = 176 (>144)


384 *1.2=460,8
new_filters = max(8, int(460,8+4)//8  * 8) = 464 (>384)

576 *1.2=691,2
new_filters = max(8, int(691,2+4)//8  * 8) = 688 (>576)

960 *1.2=1152
new_filters = max(8, int(1152+4)//8  * 8) = 1152 (>960)

1280 *1.2=1536
new_filters = max(8, int(1536+4)//8  * 8) = 1536 (>1280)




BLOCKS 1 - 7: (for b0)
      'r2_k3_s11_e1_i32_o16_se0.25', 'r4_k3_s22_e6_i16_o24_se0.25',
      'r4_k5_s22_e6_i24_o40_se0.25', 'r6_k3_s22_e6_i40_o80_se0.25',
      'r6_k5_s11_e6_i80_o112_se0.25', 'r8_k5_s22_e6_i112_o192_se0.25',
      'r2_k3_s11_e6_i192_o320_se0.25',

In details: https://github.com/tensorflow/tpu/blob/05f7b15cdf0ae36bac84beb4aef0a09983ce8f66/models/official/efficientnet/efficientnet_builder.py#L61-L69

BLOCK-1

# r1_k3_s11_e1_i32_o16_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 3
#        num_repeat=int(options['r']),				num_repeat = 2	//1
#        input_filters=int(options['i']),			input_filters = 40	//32
#        output_filters=int(options['o']),			output_filters = 16	//16
#        expand_ratio=int(options['e']),			expand_ratio = 1
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 1,1


BLOCK-2

# r2_k3_s22_e6_i16_o24_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 3
#        num_repeat=int(options['r']),				num_repeat = 3	//2
#        input_filters=int(options['i']),			input_filters = 16	//16
#        output_filters=int(options['o']),			output_filters = 32	//24
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 2,2



BLOCK-3

# r2_k5_s22_e6_i24_o40_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 5
#        num_repeat=int(options['r']),				num_repeat = 3	//2
#        input_filters=int(options['i']),			input_filters = 32	//24
#        output_filters=int(options['o']),			output_filters = 48	//40
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 2,2



BLOCK-4

# r3_k3_s22_e6_i40_o80_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 3
#        num_repeat=int(options['r']),				num_repeat = 5	//3
#        input_filters=int(options['i']),			input_filters = 48		//40
#        output_filters=int(options['o']),			output_filters = 96		//80
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 2,2



BLOCK-5

# r3_k5_s11_e6_i80_o112_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 5
#        num_repeat=int(options['r']),				num_repeat = 5	//3
#        input_filters=int(options['i']),			input_filters = 96		//80
#        output_filters=int(options['o']),			output_filters = 136	//112
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 1,1



BLOCK-6

# r4_k5_s22_e6_i112_o192_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 5
#        num_repeat=int(options['r']),				num_repeat = 6	//4
#        input_filters=int(options['i']),			input_filters = 136		//112
#        output_filters=int(options['o']),			output_filters = 232	//192
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 2,2



BLOCK-7

# r1_k3_s11_e6_i192_o320_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 3
#        num_repeat=int(options['r']),				num_repeat = 2	//1
#        input_filters=int(options['i']),			input_filters = 232		//192
#        output_filters=int(options['o']),			output_filters = 384	//320
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 1,1

CLICK ME - EfficientNet B4 model details

#alpha=1.2, beta=1.1, gamma=1.15
#d=pow(alpha, fi), w=pow(beta, fi), r=pow(gamma, fi)
#fi=4, d=2.07, w=1.46, r=1.75 - theoretically
# in practice: https://github.com/tensorflow/tpu/blob/05f7b15cdf0ae36bac84beb4aef0a09983ce8f66/models/official/efficientnet/efficientnet_builder.py#L28-L40
# efficientnet-b4': (1.4, 1.8, 380, 0.4):
#  width=1.4,  depth=1.8, resolution=380,  dropout=0.4

# https://github.com/tensorflow/tpu/blob/05f7b15cdf0ae36bac84beb4aef0a09983ce8f66/models/official/efficientnet/efficientnet_model.py#L120-L125
# repeats_new = int(math.ceil(depth * repeats)) ### ceil - Rounds x upward,

# https://github.com/tensorflow/tpu/blob/05f7b15cdf0ae36bac84beb4aef0a09983ce8f66/models/official/efficientnet/efficientnet_builder.py#L134-L137
#      width_coefficient=width_coefficient,
#      depth_coefficient=depth_coefficient,
#      depth_divisor=8,
#      min_depth=None)
#
# https://github.com/tensorflow/tpu/blob/05f7b15cdf0ae36bac84beb4aef0a09983ce8f66/models/official/efficientnet/efficientnet_model.py#L101-L117
# multiplier = width_coefficient = 1.4
# divisor = 8
# min_depth = none
# min_depth = divisor = 8

filters = filters * 1.4
new_filters = max(8, (int(filters + 4) // 8) * 8)   ## //===floor in this case
if new_filters < 0.9 * filters:  new_filters += 8

16 *1.4=22.4
new_filters = max(8, int(22.4+4)//8  * 8) = 24 (>16)

24 *1.4=33.6
new_filters = max(8, int(33.6+4)//8  * 8) = 32 (>24)

32 *1.4=44.8
new_filters = max(8, int(44.8+4)//8  * 8) = 48 (>32)

40 *1.4=56
new_filters = max(8, int(56+4)//8  * 8) = 56 (>40)

80 *1.4=112
new_filters = max(8, int(112+4)//8  * 8) = 112 (>80)

112 *1.4=156,8
new_filters = max(8, int(156,8+4)//8  * 8) = 160 (>112)

192 *1.4=268,8
new_filters = max(8, int(268,8+4)//8  * 8) = 272 (>192)

320 *1.4=448
new_filters = max(8, int(448+4)//8  * 8) = 448 (>320)



8 *1.4=11,2
new_filters = max(8, int(11,2+4)//8  * 8) = 8 (==8)

64 *1.4=89,6
new_filters = max(8, int(89,6+4)//8  * 8) = 88 (>64)

96 *1.4=134,4
new_filters = max(8, int(134,4+4)//8  * 8) = 136 (>96)


144 *1.4=201,6
new_filters = max(8, int(201,6+4)//8  * 8) = 200 (>144)


384 *1.4=537,6
new_filters = max(8, int(537,6+4)//8  * 8) = 536 (>384)

576 *1.4=806,4
new_filters = max(8, int(806,4+4)//8  * 8) = 808 (>576)

960 *1.4=1344
new_filters = max(8, int(1344+4)//8  * 8) = 1344 (>960)

1280 *1.4=1792
new_filters = max(8, int(1792+4)//8  * 8) = 1792 (>1280)




BLOCKS 1 - 7:
      'r2_k3_s11_e1_i32_o16_se0.25', 'r4_k3_s22_e6_i16_o24_se0.25',
      'r4_k5_s22_e6_i24_o40_se0.25', 'r6_k3_s22_e6_i40_o80_se0.25',
      'r6_k5_s11_e6_i80_o112_se0.25', 'r8_k5_s22_e6_i112_o192_se0.25',
      'r2_k3_s11_e6_i192_o320_se0.25',

In details: https://github.com/tensorflow/tpu/blob/05f7b15cdf0ae36bac84beb4aef0a09983ce8f66/models/official/efficientnet/efficientnet_builder.py#L61-L69

BLOCK-1

# r1_k3_s11_e1_i32_o16_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 3
#        num_repeat=int(options['r']),				num_repeat = 2	//1
#        input_filters=int(options['i']),			input_filters = 48	//32
#        output_filters=int(options['o']),			output_filters = 24	//16
#        expand_ratio=int(options['e']),			expand_ratio = 1
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 1,1


BLOCK-2

# r2_k3_s22_e6_i16_o24_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 3
#        num_repeat=int(options['r']),				num_repeat = 4	//2
#        input_filters=int(options['i']),			input_filters = 24	//16
#        output_filters=int(options['o']),			output_filters = 32	//24
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 2,2



BLOCK-3

# r2_k5_s22_e6_i24_o40_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 5
#        num_repeat=int(options['r']),				num_repeat = 4	//2
#        input_filters=int(options['i']),			input_filters = 32	//24
#        output_filters=int(options['o']),			output_filters = 56	//40
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 2,2



BLOCK-4

# r3_k3_s22_e6_i40_o80_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 3
#        num_repeat=int(options['r']),				num_repeat = 6	//3
#        input_filters=int(options['i']),			input_filters = 56		//40
#        output_filters=int(options['o']),			output_filters = 112	//80
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 2,2



BLOCK-5

# r3_k5_s11_e6_i80_o112_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 5
#        num_repeat=int(options['r']),				num_repeat = 6	//3
#        input_filters=int(options['i']),			input_filters = 112		//80
#        output_filters=int(options['o']),			output_filters = 160	//112
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 1,1



BLOCK-6

# r4_k5_s22_e6_i112_o192_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 5
#        num_repeat=int(options['r']),				num_repeat = 8	//4
#        input_filters=int(options['i']),			input_filters = 160		//112
#        output_filters=int(options['o']),			output_filters = 272	//192
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 2,2



BLOCK-7

# r1_k3_s11_e6_i192_o320_se0.25
#    return efficientnet_model.BlockArgs(
#        kernel_size=int(options['k']),				kernel_size = 3
#        num_repeat=int(options['r']),				num_repeat = 2	//1
#        input_filters=int(options['i']),			input_filters = 272		//192
#        output_filters=int(options['o']),			output_filters = 448	//320
#        expand_ratio=int(options['e']),			expand_ratio = 6
#        id_skip=('noskip' not in block_string),
#        se_ratio=float(options['se']) if 'se' in options else None,	se_ratio = 0.25
#        strides=[int(options['s'][0]), int(options['s'][1])])			strides = 1,1

In other words, to scale up the CNN, the depth of layers should increase 20%, the width 10% and the image resolution 15% to keep things as efficient as possible while expanding the implementation and improving the CNN accuracy.

The MBConv block is nothing fancy but an Inverted Residual Block (used in MobileNetV2) with a Squeeze and Excite block injected sometimes.

MobileNetV2: Inverted Residuals and Linear Bottlenecks: https://arxiv.org/pdf/1801.04381v4.pdf
MobileNetV2 graph: http://ethereon.github.io/netscope/#/gist/d01b5b8783b4582a42fe07bd46243986
MobileNetV2 proto: https://github.com/shicai/MobileNet-Caffe/blob/master/mobilenet_v2_deploy.prototxt
MobileNetv2 Darknet-cfg: https://github.com/WePCf/darknet-mobilenet-v2/blob/master/mobilenet/test.cfg (should be trained from the begining, since the src/image.c and examples/classifier.c are modified in WePCf-repo, search for "mobilenet" to see what are changed)

https://towardsdatascience.com/mobilenetv2-inverted-residuals-and-linear-bottlenecks-8a4362f4ffd5

MobileNet_v2:

EfficientNet_b0:

dexception · 2019-06-15T16:40:05Z

Would like to share this link.

https://pypi.org/project/gluoncv2/

Interesting to see the imagenet-1k comparison chart.

Model | Top 1 Error | Top 5 Error | Params | Flops
DarkNet-53 | 21.41 | 5.56 | 41,609,928 | 7,133.86M
EfficientNet-B0b | 23.41 | 6.95 | 5,288,548 | 414.31M

With the difference of 2% in top 1 error with number of parameters are 1/8 and 1/17 less flops.
Would love to see the inference time and accuracy as object detection.

Also a tiny version wouldn't be bad after all.
This is like running yolov3-tiny with yolov3 accuracy.

AlexeyAB · 2019-06-15T21:49:27Z

@dexception
Have you ever seen a graphic representation of EfficientNet b1 - b7 models (other than b0), or their exact text description, like Caffe proto-files?

dexception · 2019-06-18T06:06:43Z

@AlexeyAB

Keras, Pytorch and Mxnet implementation is definitely there:
https://github.com/qubvel/efficientnet
https://github.com/lukemelas/EfficientNet-PyTorch
https://github.com/titu1994/keras-efficientnets
https://github.com/zsef123/EfficientNets-PyTorch
https://github.com/DableUTeeF/keras-efficientnet
https://github.com/qubvel/efficientnet
https://github.com/mnikitin/EfficientNet/blob/master/efficientnet_model.py

The code and research paper is different. But the code is correct.
tensorflow/tpu#383

I don't think there is any caffe implementation as of yet.

WongKinYiu · 2019-06-18T07:06:33Z

Hello, I draw the model from Keras implementation: https://github.com/qubvel/efficientnet .
Here are b0 and b1.

CLICK ME - EfficientNet B0 and B1 model diagrams

I use the code:
`from efficientnet import EfficientNetB1
from keras.utils import plot_model

model = EfficientNetB1()
plot_model(model, to_file='EfficientNetB1.png')`

WongKinYiu · 2019-06-18T07:16:03Z

EfficientNet_b0: efficientnet_b0.cfg.txt - Accuracy: Top1 = 19.3%, Top5 = 40.6% (something goes wrong)

Maybe squeeze and excitation blocks are missing?

AlexeyAB · 2019-06-18T22:06:22Z

@WongKinYiu Thanks!

Can you also add model diagram for B4?

Maybe squeeze and excitation blocks are missing?

I think yes, there should be:

squeeze and excitation blocks https://towardsdatascience.com/squeeze-and-excitation-networks-9ef5e71eacd7 and https://arxiv.org/abs/1709.01507v4 and https://github.com/hujie-frank/SENet
dropout
batch=120 and should be trained at least 1 000 000 - 1 600 000 iterations

AlexeyAB · 2019-06-18T22:06:38Z

@dexception Thanks!

WongKinYiu · 2019-06-18T22:27:47Z

Model diagram for EfficientNets.

CLICK ME - EfficientNet B0 model diagram

CLICK ME - EfficientNet B1 model diagram

CLICK ME - EfficientNet B2 model diagram

CLICK ME - EfficientNet B3 model diagram

CLICK ME - EfficientNet B4 model diagram

CLICK ME - EfficientNet B5 model diagram

CLICK ME - EfficientNet B6 model diagram

CLICK ME - EfficientNet B7 model diagram

AlexeyAB · 2019-06-19T21:05:12Z

@WongKinYiu Thanks!

It seems now it looks like your diagram:
efficientnet_b0.cfg.txt

top1 = 69.49%
top5 = 89.44%

Should be used: should be trained at least 1.6 M iterations with learning_rate=0.256 policy=step scale=0.97 step=10000 (initial learning rate 0.256 that decays by 0.97 every 2.4 epochs) to achieve Top1 = 76.3%, Top5 = 93.2%

Trained weights-file, 500 000 iterations with batch=120: https://drive.google.com/open?id=1MvX0skcmg87T_jn8kDf2Oc6raIb56xq9

Just

I use [dropout] instead of DropConnect

On your diagrams Lambda is a [avgpool].

MBConv blocks includes:

Squeeze-and-Excitation blocks (layers: [avgpool]->[conv]->[conv]->[scale_channels])
and [dropout]-layer before each [shortcut]-residual layer
as it is done here: https://github.com/qubvel/efficientnet/blob/master/efficientnet/model.py
Swish activations

WongKinYiu · 2019-06-19T21:47:41Z

@AlexeyAB Good job! And thank you for sharing the cfg file.

I will also implement SNet of ThunderNet as backbone to compare with EfficientNet.

AlexeyAB · 2019-06-19T22:46:32Z

@WongKinYiu Yes, this is interesting that SNet+ThunderNet achieved the same accuracy 78.6% mAP@0.5 as Yolo v2, but by using 2-stage-detector with 24 FPS on ARM CPU: https://paperswithcode.com/sota/object-detection-on-pascal-voc-2007

WongKinYiu · 2019-06-19T23:14:51Z

@AlexeyAB I also want to implement CEM (Context Enhancement Module) and SAM (Spatial Attention Module) of ThunderNet.

CEM + YOLOv3 got 41.2% mAP@0.5 with 2.85 BFLOPs.
CEM + SAM + YOLOv3 got 42.0% mAP@0.5 with 2.90 BFLOPs.

CEM:

SAM:

Results:

LukeAI · 2019-06-20T09:04:16Z

I'd be interested in running a trial with efficientnet and sharing the results - do you have a B6 or B7 version of the model? Do I use it in the same way as I would with any of the other cfg files? No need to manually calculate anchors and enter classes in the cfg?

LukeAI · 2019-06-20T09:28:23Z

Oh I see - efficientnet is a full Object Detector? But maybe the B7 model with a Yolo head... ?

dexception · 2019-06-20T11:01:09Z

@LukeAI
This is imagenet classification.

LukeAI · 2019-06-20T11:19:48Z

Ok so I realise that this is image classification - I have an image classification problem with 7 classes - if necessary I could resize all my images to 32x32 - how could I train/test on my dataset with the .cfg ?

WongKinYiu · 2019-06-20T13:53:44Z

@LukeAI https://pjreddie.com/darknet/train-cifar/

mdv3101 · 2019-06-21T09:59:11Z

@AlexeyAB
Nice work on EfficientNet.
If implemented successfully this would give the fastest training and inference time among all implementations.

dexception · 2019-06-22T06:48:36Z

@AlexeyAB
Since we are already discussing the newer models here
#3114

This issue should be merged with this.
Because eventually we will have yolo-head with EfficientNet once the niggles are sorted out.

AlexeyAB · 2019-11-07T13:07:14Z

@dexception

Original MixNet uses 4 depthwise conv-layers (3x3, 5x5, 7x7, 9x9) instead of 1 depthwise conv-layer, so this has aproximately the same speed. But we can try to use uses only 4 groups for MixNet conv-layers (3x3, 5x5, 7x7, 9x9), while EfficientNet and ShuffleNetv2 uses 16-512 groups, so this modified MixNet can be faster on GPU.
Mix_net has higher Top1 accuracy and lower Flops - so Mix_net can be faster on CPU: MixNet (Mix_Conv) - 0.360 (0.5) BFlops - 77.0% (71.5%) Top1 #4203

ShuffleNetV2 - xxxx params - 0.600 BFlops - 75.4% Top1 - xxxx Top5
MixNet-L - 7.3M params - 0.565 BFlops - 78.9% Top1 - 94.2% Top5
EfficientNetB1 - 7.8M params - 0.700 BFlops - 78.8% Top1 - 94.4% Top5

syjeon121 · 2019-11-21T05:20:15Z

this paper might be relevant with this issue in detection task
https://arxiv.org/pdf/1911.09070.pdf
EfficientDet: Scalable and Efficient Object Detection

AlexeyAB · 2019-11-21T11:34:59Z

@syjeon121 Thanks! I added a separate issue: #4346

dexception · 2019-11-29T09:34:56Z

@AlexeyAB
Q1. Can you please highlight which if these architectures use depthwise-convolution layers so that we know they are for offline usage ?

Q2. I would like to know which is the most efficient architecture that doesn't use depthwise-convoltion ?

Also,
According to this link:
https://pypi.org/project/gluoncv2/

If we set the criteria for Min Top 1 Accuracy to 75%
MobileNetV3 L/224/1.0 Seem to be better than Mixnet-S.

MobileNetV3 L/224/1.0 vs Mixnet-S
226 Flops vs 260 Flops
75.37 Top 1 Accuracy vs 75.68 Accuracy.

AlexeyAB · 2019-11-29T12:06:53Z

@dexception

All these models use grouped/depthwise-conv: ShuffleNet v1/v2, EfficientNet b0-b7, MixNet-S/M/L, MobileNet v2/v3, ...

What is the offline usage ?

Do you have any paper with comparison MixNet vs MobileNetv3?

As I understand it, these are very close architectures. Yes, according to https://pypi.org/project/gluoncv2/ MobileNetV3 L/224/1.0 (14% lower BFLOPS, but 4% = 7.69 / 7.39 higher Top5-error) better than MixNet-S

Model	Top1	Top5	Params	FLOPs/2	Remarks
AlexNet	40.46	17.70	62,378,344	1,132.33M	Training (log)
--	--	--	--	--
MixNet-S	24.32	7.39	4,134,606	260.26M
--	--	--	--	--
MixNet-M	23.31	6.78	5,014,382	366.05M
--	--	--	--	--
MobileNetV3 L/224/1.0	24.63	7.69	5,481,752	226.80M
--	--	--	--	--

LukeAI · 2019-11-29T12:11:07Z

Is there a darknet implementation of MobileNetV3?

dexception · 2019-11-29T12:50:09Z

@dexception

All these models use grouped/depthwise-conv: ShuffleNet v1/v2, EfficientNet b0-b7, MixNet-S/M/L, MobileNet v2/v3, ...

What is the offline usage ?

Do you have any paper with comparison MixNet vs MobileNetv3?

As I understand it, these are very close architectures. Yes, according to https://pypi.org/project/gluoncv2/ MobileNetV3 L/224/1.0 (14% faster and 4% = 7.69 / 7.39 higher Top5-error) better than MixNet-S

Model Top1 Top5 Params FLOPs/2 Remarks
AlexNet 40.46 17.70 62,378,344 1,132.33M Training (log)

MixNet-S 24.32 7.39 4,134,606 260.26M

MixNet-M 23.31 6.78 5,014,382 366.05M

MobileNetV3 L/224/1.0 24.63 7.69 5,481,752 226.80M

By offline usage i meant user can wait for the output even if it is a little late and where accuracy is more important and we can use heavy models for detection.

Can you share which architecture is the most efficient if we don't include any that use depthwise-convolution with min top 1 accuracy 75% ?

AlexeyAB · 2019-11-29T22:31:19Z

@LukeAI No.

@dexception

At first, you should know that this accuracy MobileNetv3 Top1 75,37% and MixNet-S Top1 75,68% and EfficientNetB0 Top1 76.3% can be achieved only if you train with very large mini_batch size:
- either you use TPU-cluster ~1M$ or DGX-2 400K$ with synchronized batch-normalization (which slows down training) https://arxiv.org/abs/1711.07240v4
- or you use CPU-RAM instead of GPU-RAM which 100x time cheaper, but slows down training much more (except BM Power8-CPU with nVlink between CPU & GPUs) Beta: Using CPU-RAM instead of GPU-VRAM for large Mini_batch=32 - 128 #4386

With small mini_batch size instead of Top1 76.3% we get: #3380 (comment)

Our EfficientNet B0 (224x224) 0.9 BFLOPS - 0.45 B_FMA (16ms / RTX 2070), 4.9M params - 71.3% Top1
Official EfficientNetB0 (224x224) 0.78 BFLOPS - 0.39 FMA, 5.3M params - 70.0% Top1

Lower BFLOPS doesn't mean a faster model

So:

to compare apples with apples we should compare results with the same mini_batch size
if we are talking about speed rather than BFLOPS, then the best models:

Classifiers:
- CSPDarkNet-53 77.2% Top1: https://github.com/WongKinYiu/CrossStagePartialNetworks/blob/master/cfg/csdarknet53.cfg and https://drive.google.com/open?id=1dZJIxngmFpQJvsa6y7XADfSxkXCjJTzp comparison: https://github.com/WongKinYiu/CrossStagePartialNetworks#big-models
- CSPPeleeNet - 70.9% Top1 and CSPPeleeNet-swish-SE - 72.4% Top1 - private networks @WongKinYiu https://github.com/WongKinYiu/CrossStagePartialNetworks#small-models
Detectors:
- for 100-400 FPS on GPU - EFM (SAM) are the best models - author @WongKinYiu cfg-file is private CSPNet - New models and the most comprehensive comparison of detection models #4406 (comment)
- for ~50 FPS on GPU - CSPResNeXt50-PANet-SPP - 512x512 - 38.0% AP@0.5...0.95, 60.0% mAP@0.5 cfg/weights: CSPNet - New models and the most comprehensive comparison of detection models #4406 (comment)
- for ~30 FPS on iPhone 11 - Yolov3-spp (but I think CSPResNeXt50-PANet-SPP will be better): ThunderNet - 40.2% mAP@0.5 - 5-25 FPS on Snapdragon 845 (ARM) #3702 (comment)

More: #4406

dexception · 2019-11-30T08:02:29Z

@AlexeyAB
Thanks for sharing the stats.
Do you have any cfg files related to PeleeNet with swish activation ? I would to train it on Imagenet.

@WongKinYiu
Amazing work. Are you planning to share CSPPeleeNet - EFM (SAM) 512x512 this model in future ?

WongKinYiu · 2019-11-30T12:57:22Z

https://arxiv.org/abs/1911.11907
https://github.com/iamhankai/ghostnet

maybe better than mobilenetv3, efficientnet, ..., etc.

dexception · 2019-11-30T13:12:14Z

@AlexeyAB @WongKinYiu
Its funny but i thought i should mention this. I have temporary access to 2xTesla T4 for next 10-20 days.
After that i have to return them. So i would love to train some of these new models your talking about. Can i have some cfg files for imagenet from you guys ?

AlexeyAB · 2019-11-30T15:47:30Z

@dexception

While CSPPeleeNet is private.

And since GhostNet there isn't implemented yet.

There are MixNet-M and MixNet-M-GPU which are approximately have the same architecture, accuracy ~76.5% Top1 and speed ~0.38 FMA BFlops as large-MobileNet v3

MixNet-M: mixnet_m.cfg.txt - 0.759 BFlops (0.379 FMA) - 4.6 sec per iteration training - 45ms inference
MixNet-M-GPU (minor modification for GPU): mixnet_m_gpu.cfg.txt - 1.0 BFlops (0.500 FMA) - 2.7 sec per iteration training - 45 ms inference

Now @WongKinYiu is training MixNet-M-GPU: #4203 (comment)

Or you can try to train with CutMix and Large mini-batch size https://github.com/WongKinYiu/CrossStagePartialNetworks#big-models

For example, you can try to train https://github.com/AlexeyAB/darknet/files/3838329/mixnet_m.cfg.txt

with CutMix: Classifier - CutMix: Regularization Strategy to Train Strong Classifiers #4419

[net]
batch=120
subdivisions=2
height=224
width=224
cutmix=1

Also you can try to train the first 1-2 days and the last 1-2 days with large mini-batch size (if you have 32-128 GB CPU RAM) Beta: Using CPU-RAM instead of GPU-VRAM for large Mini_batch=32 - 128 #4386

[net]
batch=256 # or 512
subdivisions=1
height=224
width=224
cutmix=1
optimized_memory=3
workspace_size_limit_MB=2000 # or 4000

dexception · 2019-11-30T20:28:03Z

@AlexeyAB
What about PeleeNet ? Do you have any cfg files for this ?

hfassold · 2019-12-01T08:47:33Z

This paper might also Help a Bit: https://arxiv.org/pdf/1904.11486
Modification can be applied to any backbone

WongKinYiu · 2019-12-01T09:10:54Z

@hfassold

done in #3672

litingsjj · 2019-12-19T03:50:51Z

@AlexeyAB Hi， about enet-coco.cfg
139 upsample 2x 13 x 13 x 128 -> 26 x 26 x 128 140 Shortcut Layer: 90
I got this tip:
139 upsample 2x 17 x 17 x 128 -> 34 x 34 x 128 140 Shortcut Layer: 90 w = 34, w2 = 34, h = 34, h2 = 34, c = 128, c2 = 576
Is there something wrong in cfg file?

kinglintianxia · 2020-01-09T11:55:08Z

@AlexeyAB Hi, in enet-coco.cfg the last yolo layer:
mask = 1,2,3 should be mask = 0,1,2?

Fetulhak · 2021-11-10T08:51:06Z

@AlexeyAB @WongKinYiu enet-coco.cfg is very slow during training than yolov4.cfg. I am running both models in google colab. why is that? I have seen the enet-coco model have very small BFLoPs than yolov4.

AlexeyAB · 2021-11-10T13:09:43Z

@Fetulhak It is because FLOPs usually doesn't mean anything for the most of devices mobile (GPU, NPU, APU, DSP), Embedded (Jetson), mid/high-end GPUs (RTX 3070, A100....)

FLOPs says about computations cost, but it doesn't say anything about the possibility of parallelization and the need for memory bandwidth, while weak parallelization and high memory bandwidth requirements can slow down the speed many times.

For example, EfficientDetD3 has 2.4x times less Flops but 2x times slower:

YOLOv4-CSP (640x640) — 47.5% AP — 70 FPS — 120 BFlops (60 FMA)
EfficientDetD3 (896x896) — 47.5% AP — 36 FPS — 50 BFlops (25 FMA)

Fetulhak · 2021-11-10T13:21:07Z

@AlexeyAB is it because of their network architecture difference (enet-coco and yolov4), the possibility of parallelization and the need for memory bandwidth is going to be varied? Because I am using the same GPU to train both models.

AlexeyAB · 2021-11-10T13:24:23Z

@Fetulhak Yes, it is because of their network architecture difference. EfficientNet uses:

Depth-wise convolutional layers - they require a lot of memory bandwidth (as regular conv), but low number of FLOPs (much less than regular conv)
Squeeze-and-excitation blocks - they require low number of FLOPs, but can't be well parallelized

AlexeyAB added the want enhancement Want to improve accuracy, speed or functionality label Jun 11, 2019

AlexeyAB added ToDo RoadMap and removed want enhancement Want to improve accuracy, speed or functionality labels Jun 12, 2019

AlexeyAB added this to To do in Yolo works with crowds and occlusions without re-identification errors Jun 12, 2019

AlexeyAB moved this from To do to In progress in Yolo works with crowds and occlusions without re-identification errors Jun 15, 2019

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AlexeyAB mentioned this issue Jun 17, 2019

Implement Yolo-LSTM (~+4-9 AP) for detection on Video with high mAP and without blinking issues #3114

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AlexeyAB mentioned this issue Jun 25, 2019

Is it possibile to implement mobilenet-v3 in darknet? #3494

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centurions mentioned this issue Nov 13, 2019

EfficientNet Implementation ? ultralytics/yolov3#610

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AlexeyAB mentioned this issue Nov 28, 2019

CSPNet - New models and the most comprehensive comparison of detection models #4406

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AlexeyAB mentioned this issue Nov 30, 2019

Beta: Using CPU-RAM instead of GPU-VRAM for large Mini_batch=32 - 128 #4386

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AlexeyAB removed the ToDo RoadMap label Dec 2, 2019

AlexeyAB mentioned this issue Dec 24, 2019

GhostNet: More Features from Cheap Operations - 75.7% top-1 (better than than MobileNetV3) #4418

Open

dexception mentioned this issue Jan 24, 2020

Worse FPS with enet-coco.cfg than with csresnext50-panet-spp-original-optimal.cfg #4753

Closed

cenit closed this as completed Jan 23, 2021

Yolo works with crowds and occlusions without re-identification errors automation moved this from Done to To do Jan 23, 2021

cenit moved this from To do to Done in Yolo works with crowds and occlusions without re-identification errors Jan 23, 2021

EfficientNet | Implementation ? #3380

EfficientNet | Implementation ? #3380

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