You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
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
IoU threshold = 50 %, used Area-Under-Curve for each unique Recall
mean average precision ([email protected]) = 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`
The text was updated successfully, but these errors were encountered:
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 ([email protected]) = 0.000000, or 0.00 %
Total Detection Time: 100 Seconds
Set -points flag:
-points 101
for MS COCO-points 11
for PascalVOC 2007 (uncommentdifficult
in voc.data)-points 0
(AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset`The text was updated successfully, but these errors were encountered: