Training Tiny-Yolov3 with a gru layer

[Ikhadija-5]

I am trying to train Tiny Yolov3 with the addition of a gru layer. However, I do not see any results after the training process. Please find below my modifications to tiny-yolov3 config file

`[net]
batch=64
subdivisions=64

width=416
height=416
channels=1
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 = 5000
policy=steps
steps=3000,4000
scales=.1,.1

######Layer 0 ###########

[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=leaky

######Layer 1 ###########

[maxpool]
size=2
stride=2

######Layer 2 ###########

[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky

Layer 3

[maxpool]
size=2
stride=2

Layer 4

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky

Layer 5

[maxpool]
size=2
stride=2

Layer 6

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky

Layer 7

[maxpool]
size=2
stride=2

Layer 8

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky

Layer 9

[maxpool]
size=2
stride=2

Layer 10

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky

######Layer 11 ###########

[maxpool]
size=2
stride=1

######Layer 12 ###########

[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky

Layer 13 (1x1 CONVOLUTION)

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

######Layer 14 (GRU) ###########

[gru]
batch_normalize=1
output = 256

[connected]
output=256
activation=linear

######Layer 15 ###########

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky

Layer 16

[convolutional]
size=1
stride=1
pad=1
filters=18
activation=linear

Layer 17 (YOLO)

[yolo]
mask = 3,4,5
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
classes=1
num=6
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1

Layer 18

[route]
layers = -4

Layer 19

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

Layer 20 (UPSAMPLE)

[upsample]
stride=2

Layer 21 (ROUTE)

[route]
layers = -4

Layer 22

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky

Layer 23

[convolutional]
size=1
stride=1
pad=1
filters=18
activation=linear

Layer 24

[yolo]
mask = 0,1,2
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
classes=1
num=6
jitter=.3
ignore_thresh = .7
truth_thresh = 1
#random=0`

This is the result I get when I check for mAP

`CUDA-version: 11080 (12000), cuDNN: 8.9.6, CUDNN_HALF=1, GPU count: 1
CUDNN_HALF=1
OpenCV version: 4.5.4
0 : compute_capability = 700, cudnn_half = 1, GPU: Tesla V100-SXM2-16GB
net.optimized_memory = 0
mini_batch = 1, batch = 64, time_steps = 1, train = 0
layer filters size/strd(dil) input output
0 Create CUDA-stream - 0
Create cudnn-handle 0
conv 16 3 x 3/ 1 416 x 416 x 1 -> 416 x 416 x 16 0.050 BF
1 max 2x 2/ 2 416 x 416 x 16 -> 208 x 208 x 16 0.003 BF
2 conv 32 3 x 3/ 1 208 x 208 x 16 -> 208 x 208 x 32 0.399 BF
3 max 2x 2/ 2 208 x 208 x 32 -> 104 x 104 x 32 0.001 BF
4 conv 64 3 x 3/ 1 104 x 104 x 32 -> 104 x 104 x 64 0.399 BF
5 max 2x 2/ 2 104 x 104 x 64 -> 52 x 52 x 64 0.001 BF
6 conv 128 3 x 3/ 1 52 x 52 x 64 -> 52 x 52 x 128 0.399 BF
7 max 2x 2/ 2 52 x 52 x 128 -> 26 x 26 x 128 0.000 BF
8 conv 256 3 x 3/ 1 26 x 26 x 128 -> 26 x 26 x 256 0.399 BF
9 max 2x 2/ 2 26 x 26 x 256 -> 13 x 13 x 256 0.000 BF
10 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF
11 max 2x 2/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.000 BF
12 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
13 conv 256 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 256 0.089 BF
14 GRU Layer: 43264 inputs, 256 outputs
connected 43264 -> 256
connected 256 -> 256
connected 43264 -> 256
connected 256 -> 256
connected 43264 -> 256
connected 256 -> 256
15 connected 256 -> 256
16 conv 512 3 x 3/ 1 1 x 1 x 256 -> 1 x 1 x 512 0.002 BF
17 conv 18 1 x 1/ 1 1 x 1 x 512 -> 1 x 1 x 18 0.000 BF
18 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
19 route 15 -> 1 x 1 x 256
20 conv 128 1 x 1/ 1 1 x 1 x 256 -> 1 x 1 x 128 0.000 BF
21 upsample 2x 1 x 1 x 128 -> 2 x 2 x 128
22 route 18 -> 1 x 1 x 18
23 conv 256 3 x 3/ 1 1 x 1 x 18 -> 1 x 1 x 256 0.000 BF
24 conv 18 1 x 1/ 1 1 x 1 x 256 -> 1 x 1 x 18 0.000 BF
25 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 3.735
avg_outputs = 506806
Allocate additional workspace_size = 52.44 MB
Loading weights from /content/drive/MyDrive/Customv3/backup/GR-YoloV3_final.weights...
seen 64, trained: 320 K-images (5 Kilo-batches_64)
Done! Loaded 26 layers from weights-file

calculation mAP (mean average precision)...
Detection layer: 18 - type = 28
Detection layer: 25 - type = 28
392
detections_count = 0, unique_truth_count = 464
class_id = 0, name = Face, ap = 0.00% (TP = 0, FP = 0)

for conf_thresh = 0.25, precision = -nan, recall = 0.00, F1-score = -nan
for conf_thresh = 0.25, TP = 0, FP = 0, FN = 464, average IoU = 0.00 %

IoU threshold = 50 %, used Area-Under-Curve for each unique Recall
mean average precision (mAP@0.50) = 0.000000, or 0.00 %
Total Detection Time: 100 Seconds

Set -points flag:
-points 101 for MS COCO
-points 11 for PascalVOC 2007 (uncomment difficult in voc.data)
-points 0 (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset`