mAP value coming 0 while validation

[mr-mainak]

Hi,
This is a question that is asked before but I didn't find the answer. Anyways can anybody please help me out like why is the mAP value coming zero? I'm training on yolov4 tiny.

calculation mAP (mean average precision)...
 Detection layer: 30 - type = 28 
 Detection layer: 37 - type = 28 

 detections_count = 0, unique_truth_count = 406  
class_id = 0, name = fire, 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 = 406, 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 % 

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

 mean_average_precision ([email protected]) = 0.000000 
New best mAP!
Loaded: 0.000109 seconds

The config file is as follows:

[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=64
subdivisions=32
width=416
height=416
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 = 6000
policy=steps
steps=4800,5400
scales=.1,.1


#weights_reject_freq=1001
#ema_alpha=0.9998
#equidistant_point=1000
#num_sigmas_reject_badlabels=3
#badlabels_rejection_percentage=0.2


[convolutional]
batch_normalize=1
filters=32
size=3
stride=2
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky

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

[route]
layers=-1
groups=2
group_id=1

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

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

[route]
layers = -1,-2

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

[route]
layers = -6,-1

[maxpool]
size=2
stride=2

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

[route]
layers=-1
groups=2
group_id=1

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

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

[route]
layers = -1,-2

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

[route]
layers = -6,-1

[maxpool]
size=2
stride=2

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

[route]
layers=-1
groups=2
group_id=1

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

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

[route]
layers = -1,-2

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

[route]
layers = -6,-1

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=512
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=18
activation=linear

[yolo]
mask = 3,4,5
anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
classes=1
num=6
jitter=.3
scale_x_y = 1.05
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
ignore_thresh = .7
truth_thresh = 1
random=0
resize=1.5
nms_kind=greedynms
beta_nms=0.6
# new_coords=1
# scale_x_y = 2.0

[route]
layers = -4

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

[upsample]
stride=2

[route]
layers = -1, 23

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

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

[yolo]
mask = 1,2,3
anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
classes=1
num=6
jitter=.3
scale_x_y = 1.05
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
ignore_thresh = .7
truth_thresh = 1
random=0
resize=1.5
nms_kind=greedynms
beta_nms=0.6
# new_coords=1
# scale_x_y = 2.0

Any suggestions is highly appreciated.
Regards

[jklemmack]

Late answer here, but you can see that your 'fire' class had 0 detections.

detections_count = 0, unique_truth_count = 406  
class_id = 0, name = fire, ap = 0.00%   	 (TP = 0, FP = 0)