Open AurusHuang opened 6 years ago
These networks have been implemented by the author...https://pjreddie.com/darknet/ Of course, you can also design your own network and start from scratch.
Thank you for your answer.
However, I need some details in order to make such a network. Do you have any tutorials or outlines about how to adapt a classification network into YOLO (I'm not saying Darknet, because Darknet is a CNN framework)? I suppose the original YOLO network was modified from GoogleNet or something, and a few special layers were added.
Also, all the training tutorials I got need a pretrained model(for example, darknet19_448.conv.23
provided by the author). I suppose they're network-specific, not universal. What should I do if I can't get such pretrained models?
I think you might ignore the link I gave you. Please see this link... https://pjreddie.com/darknet/imagenet/ Yolo detection network was modified from classification network. ( Author has complete AlexNet,Darknet Reference, VGG-16, Extraction, Darknet19,Darknet19 448x448, Resnet,Densenet,squeezenet). You can use the above classifier network to replace the base network of yolo. The author also mentioned it in the paper.
If I designed my own network (with region layer), can I run training by just typing
.\darknet detector train %datafile.data% %cfgfile.cfg%
with %datafile.data% and %cfgfile.cfg% replaced with actual filenames?
If not, what should I do before training?
yes, you can.
But on my machine, I can't. Darknet will terminate itself even before it loads the network.
If you can, you can send your CFG configuration file to me, and I'll check it. email: nuist_lilai@foxmail.com
Well...it's probably not cfg's problem. Because it also crashes when using YOLO's original cfgs.
@AurusHuang post your error here. Firstly, check your system environment configuration. Secondly, check the installation of Darknet.
Well...the debugger has located the problem here:
char *weights = (argc > 5) ? argv[5] : 0;
if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0;
with weights
set to null pointer, it's impossible to access weights[strlen(weights) - 1]
.
post your input command here.
.\darknet detector train cfg\voc07.data tiny-yolo-voc07.cfg
will result a program termination.
.\darknet detector train cfg\voc07.data tiny-yolo-voc07.cfg darknet19_448.conv.23
will work.
sorry. In commit 330 & commit 390 src files, I haven't found your code[if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0;]. I use linux plateform not windows.
Yes. I'm using @AlexeyAB 's Darknet Windows repository. I'll checkout the latest version to see if it's modified.
@CBIR-LL can you pls give the squeeze net .cfg file and what is the size of the weight file generated ??
https://pjreddie.com/darknet/ You can find squeezenet completed by darknet in the above link page.
@CBIR-LL Thnkx i was able to find the file. i have few question. 1.Currently i am using tiny-yolo-voc.cfg which generates a model of size 63MB by using squeeze net would the model size decrease ?? 2.Or can u suggest any other .cfg file which i can use to reduce my model size
question 1: I haven't test it. question 2: change network structure; use depthwise convolution; prune, trained quantization and Huffman coding.
Yes, I modified the modle and train success.
@vowstar can u pls share the .cfg file and the info of the size of the model file generated
@AurusHuang were you able to train a custom network without a pretrained weights file?
Hello, I am trying to train the Darknet for Resnet152 configuration. I have 9-object classes each with 1000 images approx. I am using the configuration file provided in Darknet/cfg folder. Before training I did the required changes in following files according to my needs: cfvoc.data (Contains the following information: ) classes= 9 train = data/cfdata/cftrain.txt valid = data/cfdata/cfval.txt names = data/cfvoc.names backup = backup
Also, I changed the cfg and demo.c files also created the corresponding txt files required for Darknet on Pascal VOC dataset. txt files looks as follows:
1 0.408203125 0.30972222222222223 0.31640625 0.29444444444444445
The model is trained and converged well for max_batches 12000, and I could see loss value 0.06. But I am not able to run the detection using the same configuration (resnet152.cfg).
Before starting the detection the changed back the cfg for the batch=128 to 1 and subdivisions=8 also to 1.
The camera don't start but throws the assertion error.
I debugged the issue little further, and found, that it's due that there is no detection layer, and assertion is due to the cost layer present in the ResnetCFG file (I am surprised, should it be?, when there is already softmax layer in the network) and due to the
[convolutional]
filters=70
size=1
stride=1
pad=1
activation=linear
[avgpool]
[softmax]
groups=1
[cost]
type=sse
The code is crashing. So I tried to place the detection layer (Which I feel shouldn't be), [detection] classes=9
Accordingly I changed the detection layer and I further tried to place the following values; side = 7, l.coords = 4, l.n = 2, l.classes = 9, inputs = 70
now it looks like:
[detection]
classes=9
coords=4
rescore=1
side=7
num=2
softmax=1
sqrt=1
jitter=.2
object_scale=1
noobject_scale=.5
class_scale=1
coord_scale=5
Now, camera is opening but there is the detection like: -
scotti: 1689434%
skipper: 1612797%
kellogs: 24614%
valfrutta: 2470%
scotti: 20403%
granrisparmio: 2731%
tonnorio: 664%
valfrutta: 6342%
scotti: 6124%
barilla: 46584%
tonnorio: 46487%
kellogs: 3031%
colgate: 38375%
valfrutta: 14603%
Please guide me, how to change the resnet152.cfg for Detection using my trained weights.
My Command to run the detection is as follows:
./darknet detector demo cfg/cfvoc.data cfg/resnet152.cfg backup/resnet152-.backup -thresh 0.86
Please help me resolving this issue.
Thank you very much.
Kind Regards Arun
check out issue 391
Hi Groot-1313, Thanks for the link. I checked it and tried putting the [region] layer in my resnet152.cfg file in the end. The updated CFG file looks like below:
[reorg]
stride=2
[route]
layers=-1,-4
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=70
activation=linear
[region]
anchors = 1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071
bias_match=1
classes=9
coords=4
num=5
softmax=1
jitter=.3
rescore=1
object_scale=5
noobject_scale=1
class_scale=1
coord_scale=1
absolute=1
thresh = .6
random=1
But now, no detection is happening now. I tried giving very low thresholds also.
I am not sure, where I am wrong.
Kind Regards Arun
@arun-kumark
You should download Classification weights: https://pjreddie.com/media/files/resnet152.weights
Then you should get the file resnet152.200
- pre-trained weights: ./darknet partial cfg/resnet152.cfg resnet152.weights resnet152.200 200
And then you should train your Detection network on your detection dataset of images: ./darknet detector train data/obj.data detection_resnet152.cfg resnet152.200
Where detection_resnet152.cfg is resnet152 with region layer.
Hi AlexeyAB, 1.) I downloaded the weights, and followed the point2.) mentioned.
The file resnet152.200 is generated. I observed the layers at the time of generation of this file, below is the output:
arun@arun:~/darknet/darknet-master$ ./darknet partial cfg/resnet152.cfg weights/resnet152.weights resnet152.200 200
layer filters size input output
0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32
1 max 2 x 2 / 2 416 x 416 x 32 -> 208 x 208 x 32
2 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64
3 max 2 x 2 / 2 208 x 208 x 64 -> 104 x 104 x 64
4 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128
5 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64
6 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128
7 max 2 x 2 / 2 104 x 104 x 128 -> 52 x 52 x 128
8 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
9 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128
10 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256
11 max 2 x 2 / 2 52 x 52 x 256 -> 26 x 26 x 256
12 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
13 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
14 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
15 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256
16 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512
17 max 2 x 2 / 2 26 x 26 x 512 -> 13 x 13 x 512
18 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
19 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
20 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
21 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512
22 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024
23 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024
24 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024
25 route 16
26 conv 64 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 64
27 reorg / 2 26 x 26 x 64 -> 13 x 13 x 256
28 route 27 24
29 conv 1024 3 x 3 / 1 13 x 13 x1280 -> 13 x 13 x1024
30 conv 70 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 70
31 detection
mask_scale: Using default '1.000000' Loading weights from weights/resnet152.weights...Done! Saving weights to resnet152.200 arun@arun:~/darknet/darknet-master$
It doens't look like the architecture of Resnet152. As it should have more deeper network. like below:
layer filters size input output
0 conv 64 7 x 7 / 2 256 x 256 x 3 -> 128 x 128 x 64
1 max 2 x 2 / 2 128 x 128 x 64 -> 64 x 64 x 64
2 conv 64 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 64
3 conv 64 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 64
4 conv 256 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 256
5 Shortcut Layer: 1
6 conv 64 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 64
7 conv 64 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 64
8 conv 256 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 256
9 Shortcut Layer: 5
10 conv 64 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 64
11 conv 64 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 64
12 conv 256 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 256
13 Shortcut Layer: 9
14 conv 128 1 x 1 / 1 64 x 64 x 256 -> 64 x 64 x 128
15 conv 128 3 x 3 / 2 64 x 64 x 128 -> 32 x 32 x 128
16 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512
17 Shortcut Layer: 13
18 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128
19 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128
20 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512
21 Shortcut Layer: 17
22 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128
23 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128
24 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512
25 Shortcut Layer: 21
26 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128
27 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128
28 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512
29 Shortcut Layer: 25
30 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128
31 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128
32 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512
33 Shortcut Layer: 29
34 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128
35 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128
36 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512
37 Shortcut Layer: 33
38 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128
39 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128
40 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512
41 Shortcut Layer: 37
42 conv 128 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 128
43 conv 128 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 128
44 conv 512 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 512
45 Shortcut Layer: 41
46 conv 256 1 x 1 / 1 32 x 32 x 512 -> 32 x 32 x 256
47 conv 256 3 x 3 / 2 32 x 32 x 256 -> 16 x 16 x 256
48 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
49 Shortcut Layer: 45
50 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
51 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
52 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
53 Shortcut Layer: 49
54 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
55 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
56 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
57 Shortcut Layer: 53
58 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
59 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
60 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
61 Shortcut Layer: 57
62 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
63 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
64 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
65 Shortcut Layer: 61
66 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
67 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
68 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
69 Shortcut Layer: 65
70 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
71 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
72 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
73 Shortcut Layer: 69
74 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
75 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
76 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
77 Shortcut Layer: 73
78 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
79 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
80 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
81 Shortcut Layer: 77
82 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
83 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
84 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
85 Shortcut Layer: 81
86 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
87 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
88 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
89 Shortcut Layer: 85
90 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
91 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
92 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
93 Shortcut Layer: 89
94 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
95 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
96 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
97 Shortcut Layer: 93
98 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
99 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
100 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
101 Shortcut Layer: 97
102 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
103 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
104 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
105 Shortcut Layer: 101
106 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
107 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
108 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
109 Shortcut Layer: 105
110 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
111 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
112 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
113 Shortcut Layer: 109
114 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
115 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
116 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
117 Shortcut Layer: 113
118 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
119 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
120 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
121 Shortcut Layer: 117
122 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
123 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
124 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
125 Shortcut Layer: 121
126 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
127 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
128 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
129 Shortcut Layer: 125
130 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
131 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
132 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
133 Shortcut Layer: 129
134 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
135 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
136 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
137 Shortcut Layer: 133
138 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
139 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
140 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
141 Shortcut Layer: 137
142 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
143 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
144 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
145 Shortcut Layer: 141
146 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
147 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
148 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
149 Shortcut Layer: 145
150 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
151 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
152 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
153 Shortcut Layer: 149
154 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
155 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
156 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
157 Shortcut Layer: 153
158 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
159 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
160 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
161 Shortcut Layer: 157
162 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
163 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
164 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
165 Shortcut Layer: 161
166 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
167 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
168 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
169 Shortcut Layer: 165
170 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
171 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
172 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
173 Shortcut Layer: 169
174 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
175 conv 256 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 256
176 conv 1024 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x1024
177 Shortcut Layer: 173
178 conv 256 1 x 1 / 1 16 x 16 x1024 -> 16 x 16 x 256
Please confirm, if this is the right observation, and I should proceed with the training?
Kind Regards Arun
@arun-kumark
Something goes wrong!
partial
: https://github.com/AlexeyAB/darknetIt should looks like this:
Hi Alexey,
After forking, issue is resolved. Seems there were inconsistent files in my repository. Now right layers are displayed at the time of generation. My GPU is on the training.
192 conv 2048 1 x 1 / 1 8 x 8 x 512 -> 8 x 8 x2048 193 Shortcut Layer: 189 194 conv 512 1 x 1 / 1 8 x 8 x2048 -> 8 x 8 x 512 195 conv 512 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x 512 196 conv 2048 1 x 1 / 1 8 x 8 x 512 -> 8 x 8 x2048 197 Shortcut Layer: 193 198 conv 512 1 x 1 / 1 8 x 8 x2048 -> 8 x 8 x 512 199 conv 512 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x 512 200 conv 2048 1 x 1 / 1 8 x 8 x 512 -> 8 x 8 x2048 201 Shortcut Layer: 197 202 conv 70 1 x 1 / 1 8 x 8 x2048 -> 8 x 8 x 70 203 detection mask_scale: Using default '1.000000' Loading weights from resnet152.200...Done!
Learning Rate: 0.0001, Momentum: 0.9, Decay: 0.0005
Loaded: 0.461994 seconds
Region Avg IOU: 0.001865, Class: 0.148485, Obj: 0.295209, No Obj: 0.544053, Avg Recall: 0.000000, count: 41
Region Avg IOU: 0.001082, Class: 0.089779, Obj: 0.531414, No Obj: 0.558429, Avg Recall: 0.000000, count: 32
Region Avg IOU: 0.001236, Class: 0.162143, Obj: 0.469012, No Obj: 0.541242, Avg Recall: 0.000000, count: 43
Region Avg IOU: 0.003289, Class: 0.138179, Obj: 0.354948, No Obj: 0.538169, Avg Recall: 0.000000, count: 38
Region Avg IOU: 0.000340, Class: 0.109414, Obj: 0.237255, No Obj: 0.549358, Avg Recall: 0.000000, count: 40
Region Avg IOU: 0.001976, Class: 0.126576, Obj: 0.408487, No Obj: 0.540027, Avg Recall: 0.000000, count: 54
Region Avg IOU: 0.007314, Class: 0.120645, Obj: 0.289803, No Obj: 0.531240, Avg Recall: 0.000000, count: 37
Region Avg IOU: 0.004117, Class: 0.098440, Obj: 0.432943, No Obj: 0.540761, Avg Recall: 0.000000, count: 51
1: 2457.756592, 2457.756592 avg, 0.000000 rate, 2.658998 seconds, 128 images
Loaded: 0.000022 seconds
Region Avg IOU: 0.011552, Class: 0.083911, Obj: 0.404444, No Obj: 0.538594, Avg Recall: 0.000000, count: 59
Region Avg IOU: 0.005468, Class: 0.159013, Obj: 0.400990, No Obj: 0.531313, Avg Recall: 0.000000, count: 82
Region Avg IOU: 0.010414, Class: 0.187706, Obj: 0.385098, No Obj: 0.532727, Avg Recall: 0.000000, count: 51
Region Avg IOU: 0.009937, Class: 0.060624, Obj: 0.364081, No Obj: 0.553725, Avg Recall: 0.000000, count: 33
Region Avg IOU: 0.002738, Class: 0.094282, Obj: 0.680848, No Obj: 0.552582, Avg Recall: 0.000000, count: 35
Region Avg IOU: 0.006391, Class: 0.166192, Obj: 0.327868, No Obj: 0.525272, Avg Recall: 0.000000, count: 42
Region Avg IOU: 0.005366, Class: 0.125848, Obj: 0.451338, No Obj: 0.543940, Avg Recall: 0.000000, count: 50
I will update the detection results once again, the training is completed.
Thank you.
Kind Regards Arun
Hi,
I tested my model yesterday evening, the prediction was good, but there is a problem with the detection.
When I increase the max_crop
to 640 or more, the camera detects nicely from a distance, but when I reduces this parameter in the CFG file, there very less detection from distance, but bringing the object nearer to camera give many detection (right detection but many bounding boxes). The average loss after 10000 batches was 4.700.
Then I changed the CFG file and again started the training on two GPUs, with the following configuration:
**[net] batch=128 #earlier it was 64 subdivisions=32 #earlier it was 8
height=448 #earlier it was 225 width=448 #earlier it was 225 max_crop=448 channels=3 momentum=0.9 decay=0.0005
burn_in=1000 learning_rate=0.0001 policy=poly power=4 max_batches=14000**
Now after 11K Batches, here are the logs, where the Average loss still not going below 4.7 and IOU also remains around 0.7 (Can it go to 0.9?) If yes, what modification in dataset is needed. Please guide. Below are the logs after 11K iterations:
Region Avg IOU: 0.660135, Class: 0.981329, Obj: 0.487581, No Obj: 0.010456, Avg Recall: 0.894737, count: 19
Region Avg IOU: 0.763370, Class: 0.995517, Obj: 0.739083, No Obj: 0.006776, Avg Recall: 1.000000, count: 5
Region Avg IOU: 0.742606, Class: 0.999824, Obj: 0.713308, No Obj: 0.005774, Avg Recall: 1.000000, count: 2
Region Avg IOU: 0.612590, Class: 0.874384, Obj: 0.590807, No Obj: 0.013855, Avg Recall: 0.800000, count: 20
Region Avg IOU: 0.662515, Class: 0.974764, Obj: 0.572539, No Obj: 0.007961, Avg Recall: 0.833333, count: 12
Region Avg IOU: 0.807390, Class: 0.999769, Obj: 0.728954, No Obj: 0.006462, Avg Recall: 1.000000, count: 6
Region Avg IOU: 0.761094, Class: 0.993665, Obj: 0.711664, No Obj: 0.018441, Avg Recall: 0.923077, count: 13
Region Avg IOU: 0.610503, Class: 0.855781, Obj: 0.623467, No Obj: 0.006637, Avg Recall: 0.666667, count: 9
Region Avg IOU: 0.741221, Class: 0.991534, Obj: 0.656385, No Obj: 0.015778, Avg Recall: 1.000000, count: 20
Region Avg IOU: 0.646886, Class: 0.921648, Obj: 0.606587, No Obj: 0.008618, Avg Recall: 0.692308, count: 13
Region Avg IOU: 0.546909, Class: 0.773235, Obj: 0.517832, No Obj: 0.009554, Avg Recall: 0.647059, count: 17
Region Avg IOU: 0.753439, Class: 0.984664, Obj: 0.596383, No Obj: 0.012855, Avg Recall: 1.000000, count: 15
Region Avg IOU: 0.740693, Class: 0.892905, Obj: 0.633424, No Obj: 0.012075, Avg Recall: 1.000000, count: 11
Region Avg IOU: 0.417592, Class: 0.568096, Obj: 0.360936, No Obj: 0.003444, Avg Recall: 0.466667, count: 15
Region Avg IOU: 0.757063, Class: 0.989477, Obj: 0.742855, No Obj: 0.005838, Avg Recall: 1.000000, count: 7
Region Avg IOU: 0.490070, Class: 0.727844, Obj: 0.467702, No Obj: 0.002171, Avg Recall: 0.400000, count: 5
Region Avg IOU: 0.749233, Class: 0.993034, Obj: 0.557237, No Obj: 0.006512, Avg Recall: 1.000000, count: 9
Region Avg IOU: 0.798812, Class: 0.999825, Obj: 0.740936, No Obj: 0.003703, Avg Recall: 1.000000, count: 1
Region Avg IOU: 0.702355, Class: 0.963193, Obj: 0.619650, No Obj: 0.012679, Avg Recall: 0.882353, count: 17
Region Avg IOU: 0.694633, Class: 0.921360, Obj: 0.614172, No Obj: 0.013266, Avg Recall: 0.850000, count: 20
Region Avg IOU: 0.682420, Class: 0.880997, Obj: 0.618540, No Obj: 0.013046, Avg Recall: 0.933333, count: 15
Region Avg IOU: 0.759445, Class: 0.997710, Obj: 0.749968, No Obj: 0.009021, Avg Recall: 1.000000, count: 10
Region Avg IOU: 0.589034, Class: 0.641790, Obj: 0.381082, No Obj: 0.006324, Avg Recall: 0.800000, count: 5
Region Avg IOU: 0.565351, Class: 0.831913, Obj: 0.548575, No Obj: 0.008631, Avg Recall: 0.588235, count: 17
Region Avg IOU: 0.745497, Class: 0.999130, Obj: 0.703703, No Obj: 0.006737, Avg Recall: 0.857143, count: 7
Region Avg IOU: 0.758075, Class: 0.992727, Obj: 0.601360, No Obj: 0.019998, Avg Recall: 1.000000, count: 15
Region Avg IOU: 0.766810, Class: 0.997058, Obj: 0.723047, No Obj: 0.013459, Avg Recall: 0.928571, count: 14
Region Avg IOU: 0.749548, Class: 0.968871, Obj: 0.657072, No Obj: 0.009012, Avg Recall: 0.933333, count: 15
Region Avg IOU: 0.644974, Class: 0.899949, Obj: 0.606202, No Obj: 0.011949, Avg Recall: 0.833333, count: 12
Region Avg IOU: 0.653251, Class: 0.999973, Obj: 0.650427, No Obj: 0.002155, Avg Recall: 1.000000, count: 1
Region Avg IOU: 0.528771, Class: 0.746722, Obj: 0.499436, No Obj: 0.010765, Avg Recall: 0.550000, count: 20
Region Avg IOU: 0.529873, Class: 0.702395, Obj: 0.408791, No Obj: 0.007409, Avg Recall: 0.461538, count: 13
Region Avg IOU: 0.445353, Class: 0.633009, Obj: 0.489467, No Obj: 0.010415, Avg Recall: 0.466667, count: 15
Region Avg IOU: 0.625656, Class: 0.865984, Obj: 0.643256, No Obj: 0.022857, Avg Recall: 0.800000, count: 25
Region Avg IOU: 0.657238, Class: 0.910786, Obj: 0.601219, No Obj: 0.011132, Avg Recall: 0.857143, count: 14
Region Avg IOU: 0.356616, Class: 0.480169, Obj: 0.371067, No Obj: 0.006882, Avg Recall: 0.333333, count: 9
Region Avg IOU: 0.799334, Class: 0.996106, Obj: 0.736987, No Obj: 0.009270, Avg Recall: 1.000000, count: 5
Region Avg IOU: 0.495373, Class: 0.738899, Obj: 0.510692, No Obj: 0.008909, Avg Recall: 0.571429, count: 7
Region Avg IOU: 0.727941, Class: 0.999592, Obj: 0.653946, No Obj: 0.006865, Avg Recall: 1.000000, count: 6
Region Avg IOU: 0.640597, Class: 0.888973, Obj: 0.571678, No Obj: 0.009311, Avg Recall: 0.722222, count: 18
Region Avg IOU: 0.663166, Class: 0.860342, Obj: 0.642832, No Obj: 0.006604, Avg Recall: 0.857143, count: 7
Region Avg IOU: 0.534534, Class: 0.749022, Obj: 0.552152, No Obj: 0.007498, Avg Recall: 0.687500, count: 16
Region Avg IOU: 0.668086, Class: 0.965958, Obj: 0.653159, No Obj: 0.009047, Avg Recall: 0.900000, count: 10
Region Avg IOU: 0.743687, Class: 0.999275, Obj: 0.675397, No Obj: 0.002428, Avg Recall: 1.000000, count: 5
Region Avg IOU: 0.722658, Class: 0.988107, Obj: 0.624207, No Obj: 0.014219, Avg Recall: 0.941176, count: 17
Region Avg IOU: 0.539097, Class: 0.765993, Obj: 0.492647, No Obj: 0.006569, Avg Recall: 0.538462, count: 13
Region Avg IOU: 0.605611, Class: 0.799359, Obj: 0.525507, No Obj: 0.007891, Avg Recall: 0.666667, count: 12
Region Avg IOU: 0.712711, Class: 0.965310, Obj: 0.629983, No Obj: 0.018342, Avg Recall: 0.812500, count: 16
Region Avg IOU: 0.827956, Class: 0.999183, Obj: 0.492812, No Obj: 0.009154, Avg Recall: 1.000000, count: 5
Region Avg IOU: 0.696157, Class: 0.979458, Obj: 0.706899, No Obj: 0.017219, Avg Recall: 0.863636, count: 22
Region Avg IOU: 0.470520, Class: 0.716070, Obj: 0.455911, No Obj: 0.002836, Avg Recall: 0.500000, count: 8
Region Avg IOU: 0.771328, Class: 0.999320, Obj: 0.742848, No Obj: 0.004383, Avg Recall: 1.000000, count: 1
Region Avg IOU: 0.668899, Class: 0.881764, Obj: 0.599017, No Obj: 0.013235, Avg Recall: 0.800000, count: 10
Region Avg IOU: 0.764554, Class: 0.993098, Obj: 0.630215, No Obj: 0.015358, Avg Recall: 1.000000, count: 15
Region Avg IOU: 0.687430, Class: 0.967540, Obj: 0.659137, No Obj: 0.016945, Avg Recall: 0.800000, count: 20
Region Avg IOU: 0.769750, Class: 0.998442, Obj: 0.578108, No Obj: 0.008273, Avg Recall: 1.000000, count: 4
Region Avg IOU: 0.702567, Class: 0.960225, Obj: 0.463868, No Obj: 0.004220, Avg Recall: 0.800000, count: 5
Region Avg IOU: 0.580877, Class: 0.832473, Obj: 0.522247, No Obj: 0.009376, Avg Recall: 0.733333, count: 15
Region Avg IOU: 0.664587, Class: 0.946535, Obj: 0.540527, No Obj: 0.008747, Avg Recall: 0.909091, count: 11
Region Avg IOU: 0.790199, Class: 0.989789, Obj: 0.593592, No Obj: 0.017175, Avg Recall: 1.000000, count: 11
Region Avg IOU: 0.693093, Class: 0.973041, Obj: 0.571220, No Obj: 0.004351, Avg Recall: 0.857143, count: 7
Region Avg IOU: 0.752242, Class: 0.992657, Obj: 0.701140, No Obj: 0.012526, Avg Recall: 1.000000, count: 9
Region Avg IOU: 0.588476, Class: 0.842634, Obj: 0.581750, No Obj: 0.006982, Avg Recall: 0.700000, count: 10
Region Avg IOU: 0.768080, Class: 0.925007, Obj: 0.682537, No Obj: 0.006848, Avg Recall: 1.000000, count: 8
Syncing... Done!
12072: 4.486686, 4.682826 avg, 0.000000 rate, 9.533536 seconds, 3090432 images
12072: 4.486686, 4.682826 avg, 0.000000 rate, 9.533536 seconds, 3090432 images
Thanks Kind Regards Arun
Dear @CBIR-LL Are you sure squeezenet network is completed by darknet? I have checked that the darkne/cfg directory. It does not have squeezenet, but have resnet cfg , so I don't know where to find it.
Dear @AlexeyAB , Can you help me to train yolo with backbone resnet101. I was attempt to follow your instructions from your darknet but there is nothing for training with resnet101, I also tried to partial resnet101 and download weight from darknet but I got just 0.0% for both AP in each class and mAP after training over.
Hi Duynn912, Please check the cfg file, which I used sometimes back for resnet152, check the last layer of CFG, hope you will get the clue to change your cfg.
[net]
# Testing
batch=1
subdivisions=1
height=448
width=448
max_crop=448
channels=3
momentum=0.9
decay=0.0005
burn_in=1000
learning_rate=0.0001
policy=poly
power=4
max_batches=14000
angle=7
hue=.1
saturation=.75
exposure=.75
aspect=.75
[convolutional]
batch_normalize=1
filters=64
size=7
stride=2
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
# Conv 4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
#Conv 5
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=2048
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=2048
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=2048
size=1
stride=1
pad=1
activation=linear
[shortcut]
from=-4
activation=leaky
[convolutional]
filters=70
size=1
stride=1
pad=1
activation=linear
[region]
anchors = 1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071
bias_match=1
classes=9
coords=4
num=5
softmax=1
jitter=.2
rescore=1
object_scale=5
noobject_scale=1
class_scale=1
coord_scale=1
absolute=1
thresh = .001
random=0
Dear Arun Thank you for your cfg. It works, but the loss was still high and around 0.4. I tried to add a batch normalization after shortcut, the loss was lower and reached 0.1, Do you know any reason why the situation occur? Vào 15:16 Th 6, ngày 19 tháng 10 năm 2018 Arun notifications@github.com đã viết:
Hi Duynn912, Please check the cfg file, which I used sometimes back for resnet152, check the last layer of CFG, hope you will get the clue to change your cfg.
[net]
Testing
batch=1 subdivisions=1
height=448 width=448 max_crop=448 channels=3 momentum=0.9 decay=0.0005
burn_in=1000 learning_rate=0.0001 policy=poly power=4 max_batches=14000
angle=7 hue=.1 saturation=.75 exposure=.75 aspect=.75
[convolutional] batch_normalize=1 filters=64 size=7 stride=2 pad=1 activation=leaky
[maxpool] size=2 stride=2
[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=3 stride=2 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
Conv 4
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=2 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
Conv 5
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=3 stride=2 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=2048 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=2048 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=2048 size=1 stride=1 pad=1 activation=linear
[shortcut] from=-4 activation=leaky
[convolutional] filters=70 size=1 stride=1 pad=1 activation=linear
[region] anchors = 1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071 bias_match=1 classes=9 coords=4 num=5 softmax=1 jitter=.2 rescore=1
object_scale=5 noobject_scale=1 class_scale=1 coord_scale=1
absolute=1 thresh = .001 random=0
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I am not sure if these links and procedure works still, I am sharing from my old notes (Apr-2018). I hope if you have created the partial file before starting the training...
PATH OF THE PARTIAL FILE
Issue: https://github.com/pjreddie/darknet/issues/323 https://pjreddie.com/media/files/resnet152.weights
CREATION OF THE PARTIAL FILE
./darknet partial cfg/cf_resnet152.cfg weights/resnet152.weights resnet152.200 200
TRAIN ON MULTIPLE GPUS
./darknet detector train cfg/cf_voc.data cfg/cf_resnet152-train.cfg resnet152.200 -gpu 0,1
Hi Arun,
I have followed to do the steps like you mentioned but I did not understand the situation I got.
Vào Th 2, 22 thg 10, 2018 vào lúc 14:53 Arun notifications@github.com đã viết:
I am not sure if these links and procedure works still, I am sharing from my old notes (Apr-2018). I hope if you have created the partial file before starting the training...
PATH OF THE PARTIAL FILE
Issue: #323 https://github.com/pjreddie/darknet/issues/323 https://pjreddie.com/media/files/resnet152.weights
CREATION OF THE PARTIAL FILE
./darknet partial cfg/cf_resnet152.cfg weights/resnet152.weights resnet152.200 200
TRAIN ON MULTIPLE GPUS
./darknet detector train cfg/cf_voc.data cfg/cf_resnet152-train.cfg resnet152.200 -gpu 0,1
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Hi, I wanted to modify the network architecture of the YOLOv2 like using a few layers, removing a few layers and then adding some new layers to the YOLOv2 network. So i was wondering is it possible to do so by just making a few changes in the .cfg and running the command .\darknet detector train %datafile.data% %cfgfile.cfg%
Hi, I wanted to modify the network architecture of the YOLOv2 like using a few layers, removing a few layers and then adding some new layers to the YOLOv2 network. So i was wondering is it possible to do so by just making a few changes in the .cfg and running the command .\darknet detector train %datafile.data% %cfgfile.cfg%
hello prateekgupta891 yes you can do it, it mentioned here
For training Yolo based on other models (DenseNet201-Yolo or ResNet50-Yolo), you can download and get pre-trained weights as showed in this file: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd If you made you custom model that isn't based on other models, then you can train it without pre-trained weights, then will be used random initial weights.
@mtlcrusher I tried using yolov3 with rsnet50 backbone, by following the steps mentioned in the link you shared. but im getting this error. they say it is because of weighted shortcut layer update. can u help me to resolve it.
@mtlcrusher I tried using yolov3 with rsnet50 backbone, by following the steps mentioned in the link you shared. but im getting this error. they say it is because of weighted shortcut layer update. can u help me to resolve it.
i think, it's because the dimension didn't match.. rechecked the model may solve it
@AurusHuang did you find an answer if you can train without any pre-trained weights and train from scratch ?
It's been 3 years, i think this issue need to be closed
@AurusHuang did you find an answer if you can train without any pre-trained weights and train from scratch ?
yes you can, just run this command darknet detector train %datafile.data% %cfgfile.cfg%
if you want to include mAP calculation, use the option -map like this darknet detector train %datafile.data% %cfgfile.cfg% -map
Hope these help your day :)
Hi, I wanted to modify the network architecture of the YOLOv2 like using a few layers and adding some new layers to the last YOLOv2 network. So i was wondering is it possible to use pretrained for top layers of YOLOv2? It like fine-tune in tensorflow..
I am working on a research that requires me changing yolo v4 backbone from cspdarknet to efficientnet but don't know how to adjust the cfg file to achieve that. Would appreciate if anyone could provide some insights into that
efficientnet but don't know how to adjust the cfg file to achieve that.
you can edit the cfg file with layers referenced in the wiki ( https://github.com/AlexeyAB/darknet/wiki ).
Thanks, but what exact parameters do i need to adjust and to what values
Thanks, but what exact parameters do i need to adjust and to what values
Thanks, but what exact parameters do i need to adjust and to what values
you can change the values in one of the cfg files in the cfg folder or you can write from scratch based on the article
I have an idea of adapting YOLO network to some other existing classification networks like SqueezeNet or ResNet to tackle different problems. Is it possible to create such network and train without pretrained weights file? What specific layers or parameters should I be aware of?