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cfg cleanup

This commit is contained in:
Glenn Jocher 2020-04-20 16:34:00 -07:00
parent be3f322375
commit cdb69d5929
3 changed files with 212 additions and 695 deletions
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441
cfg/yolov3s.cfg → cfg/yolov3-asff.cfg
View File

@ -1,3 +1,9 @@
# Generated by Glenn Jocher (glenn.jocher@ultralytics.com) for https://github.com/ultralytics/yolov3
# def kmean_anchors(path='../coco/train2017.txt', n=12, img_size=(320, 640)): # from utils.utils import *; kmean_anchors()
# Evolving anchors: 100%|██████████| 1000/1000 [41:15<00:00, 2.48s/it]
# 0.20 iou_thr: 0.992 best possible recall, 4.25 anchors > thr
# kmeans anchors (n=12, img_size=(320, 640), IoU=0.005/0.184/0.634-min/mean/best): 6,9, 15,16, 17,35, 37,26, 36,67, 63,42, 57,100, 121,81, 112,169, 241,158, 195,310, 426,359
[net]
# Testing
# batch=1
@ -28,7 +34,7 @@ filters=32
size=3
stride=1
pad=1
activation=swish
activation=leaky
# Downsample
@ -38,7 +44,7 @@ filters=64
size=3
stride=2
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -46,7 +52,7 @@ filters=32
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -54,7 +60,7 @@ filters=64
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
@ -68,7 +74,7 @@ filters=128
size=3
stride=2
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -76,7 +82,7 @@ filters=64
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -84,7 +90,7 @@ filters=128
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
@ -96,7 +102,7 @@ filters=64
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -104,7 +110,7 @@ filters=128
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
@ -118,7 +124,7 @@ filters=256
size=3
stride=2
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -126,7 +132,7 @@ filters=128
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -134,7 +140,7 @@ filters=256
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
@ -146,7 +152,7 @@ filters=128
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -154,7 +160,7 @@ filters=256
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
@ -166,7 +172,7 @@ filters=128
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -174,7 +180,7 @@ filters=256
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
@ -186,7 +192,7 @@ filters=128
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -194,28 +200,7 @@ filters=256
size=3
stride=1
pad=1
activation=swish
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=swish
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
@ -227,7 +212,7 @@ filters=128
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -235,7 +220,7 @@ filters=256
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
@ -247,7 +232,7 @@ filters=128
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -255,7 +240,7 @@ filters=256
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
@ -267,7 +252,7 @@ filters=128
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -275,7 +260,27 @@ filters=256
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
@ -289,7 +294,7 @@ filters=512
size=3
stride=2
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -297,7 +302,7 @@ filters=256
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -305,70 +310,7 @@ filters=512
size=3
stride=1
pad=1
activation=swish
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=swish
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=swish
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=swish
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=swish
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=swish
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
@ -380,7 +322,7 @@ filters=256
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -388,49 +330,7 @@ filters=512
size=3
stride=1
pad=1
activation=swish
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=swish
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=swish
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=swish
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
@ -442,7 +342,7 @@ filters=256
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -450,7 +350,107 @@ filters=512
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
@ -464,7 +464,7 @@ filters=1024
size=3
stride=2
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -472,7 +472,7 @@ filters=512
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -480,7 +480,7 @@ filters=1024
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
@ -492,7 +492,7 @@ filters=512
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -500,7 +500,7 @@ filters=1024
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
@ -512,7 +512,7 @@ filters=512
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -520,7 +520,7 @@ filters=1024
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
@ -532,7 +532,7 @@ filters=512
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -540,7 +540,7 @@ filters=1024
size=3
stride=1
pad=1
activation=swish
activation=leaky
[shortcut]
from=-3
@ -554,7 +554,7 @@ filters=512
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -562,7 +562,7 @@ size=3
stride=1
pad=1
filters=1024
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -570,9 +570,9 @@ filters=512
size=1
stride=1
pad=1
activation=swish
activation=leaky
### SPP ###
# SPP --------------------------------------------------------------------------
[maxpool]
stride=1
size=5
@ -593,8 +593,7 @@ size=13
[route]
layers=-1,-3,-5,-6
### End SPP ###
# SPP --------------------------------------------------------------------------
[convolutional]
batch_normalize=1
@ -602,8 +601,7 @@ filters=512
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -611,7 +609,7 @@ size=3
stride=1
pad=1
filters=1024
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -619,7 +617,7 @@ filters=512
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -627,29 +625,19 @@ size=3
stride=1
pad=1
filters=1024
activation=swish
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
filters=258
activation=linear
[yolo]
mask = 6,7,8
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
# YOLO -------------------------------------------------------------------------
[route]
layers = -4
layers = -3
[convolutional]
batch_normalize=1
@ -657,7 +645,7 @@ filters=256
size=1
stride=1
pad=1
activation=swish
activation=leaky
[upsample]
stride=2
@ -665,15 +653,13 @@ stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -681,7 +667,7 @@ size=3
stride=1
pad=1
filters=512
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -689,7 +675,7 @@ filters=256
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -697,7 +683,7 @@ size=3
stride=1
pad=1
filters=512
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -705,7 +691,7 @@ filters=256
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -713,30 +699,19 @@ size=3
stride=1
pad=1
filters=512
activation=swish
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
filters=258
activation=linear
[yolo]
mask = 3,4,5
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
# YOLO -------------------------------------------------------------------------
[route]
layers = -4
layers = -3
[convolutional]
batch_normalize=1
@ -744,7 +719,7 @@ filters=128
size=1
stride=1
pad=1
activation=swish
activation=leaky
[upsample]
stride=2
@ -752,15 +727,13 @@ stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -768,7 +741,7 @@ size=3
stride=1
pad=1
filters=256
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -776,7 +749,7 @@ filters=128
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -784,7 +757,7 @@ size=3
stride=1
pad=1
filters=256
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -792,7 +765,7 @@ filters=128
size=1
stride=1
pad=1
activation=swish
activation=leaky
[convolutional]
batch_normalize=1
@ -800,22 +773,32 @@ size=3
stride=1
pad=1
filters=256
activation=swish
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
filters=258
activation=linear
[yolo]
mask = 0,1,2
from=88,99,110
mask = 8,9,10,11
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
[yolo]
from=88,99,110
mask = 4,5,6,7
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
[yolo]
from=88,99,110
mask = 0,1,2,3
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1

233
cfg/yolov4-tiny-1cls.cfg
View File

@ -1,233 +0,0 @@
# Generated by Glenn Jocher (glenn.jocher@ultralytics.com) for https://github.com/ultralytics/yolov3
# def kmean_anchors(path='../coco/train2017.txt', n=12, img_size=(320, 640)): # from utils.utils import *; kmean_anchors()
# Evolving anchors: 100%|██████████| 1000/1000 [41:15<00:00, 2.48s/it]
# 0.20 iou_thr: 0.992 best possible recall, 4.25 anchors > thr
# kmeans anchors (n=12, img_size=(320, 640), IoU=0.005/0.184/0.634-min/mean/best): 6,9, 15,16, 17,35, 37,26, 36,67, 63,42, 57,100, 121,81, 112,169, 241,158, 195,310, 426,359
[net]
# Testing
# batch=1
# subdivisions=1
# Training
batch=64
subdivisions=16
width=608
height=608
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 200000
policy=steps
steps=180000,190000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=1
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
###########
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=24
activation=linear
[yolo]
mask = 8,9,10,11
anchors = 6,9, 15,16, 17,35, 37,26, 36,67, 63,42, 57,100, 121,81, 112,169, 241,158, 195,310, 426,359
classes=1
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 8
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=24
activation=linear
[yolo]
mask = 4,5,6,7
anchors = 6,9, 15,16, 17,35, 37,26, 36,67, 63,42, 57,100, 121,81, 112,169, 241,158, 195,310, 426,359
classes=1
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -3
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 6
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=24
activation=linear
[yolo]
mask = 0,1,2,3
anchors = 6,9, 15,16, 17,35, 37,26, 36,67, 63,42, 57,100, 121,81, 112,169, 241,158, 195,310, 426,359
classes=1
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1

233
cfg/yolov4-tiny.cfg
View File

@ -1,233 +0,0 @@
# Generated by Glenn Jocher (glenn.jocher@ultralytics.com) for https://github.com/ultralytics/yolov3
# def kmean_anchors(path='../coco/train2017.txt', n=12, img_size=(320, 640)): # from utils.utils import *; kmean_anchors()
# Evolving anchors: 100%|██████████| 1000/1000 [41:15<00:00, 2.48s/it]
# 0.20 iou_thr: 0.992 best possible recall, 4.25 anchors > thr
# kmeans anchors (n=12, img_size=(320, 640), IoU=0.005/0.184/0.634-min/mean/best): 6,9, 15,16, 17,35, 37,26, 36,67, 63,42, 57,100, 121,81, 112,169, 241,158, 195,310, 426,359
[net]
# Testing
# batch=1
# subdivisions=1
# Training
batch=64
subdivisions=16
width=608
height=608
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 200000
policy=steps
steps=180000,190000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=1
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
###########
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=340
activation=linear
[yolo]
mask = 8,9,10,11
anchors = 6,9, 15,16, 17,35, 37,26, 36,67, 63,42, 57,100, 121,81, 112,169, 241,158, 195,310, 426,359
classes=80
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 8
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=340
activation=linear
[yolo]
mask = 4,5,6,7
anchors = 6,9, 15,16, 17,35, 37,26, 36,67, 63,42, 57,100, 121,81, 112,169, 241,158, 195,310, 426,359
classes=80
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -3
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 6
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=340
activation=linear
[yolo]
mask = 0,1,2,3
anchors = 6,9, 15,16, 17,35, 37,26, 36,67, 63,42, 57,100, 121,81, 112,169, 241,158, 195,310, 426,359
classes=80
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1