用标准yolov5s转换为et模型后进行半监督训练，在训练过程中所有指标变为0

# 我先自己训练了一个标准的yolov5s模型，然后用convert_yolov5_to_efficient.py脚本转换为efficient-yolov5s.pt，再进行半监督训练，配置文件如下：

project: './runs/yolov5_ssod'
adam: False
epochs: 200
weights: './data/et/fire_smoke/efficient-yolov5s.pt'
prune_finetune: False
linear_lr: True
hyp:
lr0: 0.001
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
lrf: 1.0
scale: 0.9
burn_epochs: 20
no_aug_epochs: 0
warmup_epochs: 3

Model:
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
Backbone:
name: 'YoloV5'
activation: 'SiLU'
Neck:
name: 'YoloV5'
in_channels: [256, 512, 1024]
out_channels: [256, 512, 1024]
activation: 'SiLU'
Head:
name: 'YoloV5'
activation: 'SiLU'
anchors: [[10,13, 16,30, 33,23],[30,61, 62,45, 59,119],[116,90, 156,198, 373,326]] # P5/32]
Loss:
type: 'ComputeLoss'
cls: 0.3
obj: 0.7
anchor_t: 4.0

Dataset:
data_name: 'coco'
train: ../od_data/fire_smoke_filter/train.txt # 118287 images
val: ../od_data/fire_smoke_filter/test.txt # 5000 images
test: ../od_data/fire_smoke_filter/test.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794^
target: ../od_data/fire_smoke_filter/unlabel.txt
nc: 2 # number of classes
np: 0 #number of keypoints
names: ['fire', 'smoke']
img_size: 640
batch_size: 8

SSOD:
train_domain: True
nms_conf_thres: 0.1
nms_iou_thres: 0.65
teacher_loss_weight: 3.0
cls_loss_weight: 0.3
box_loss_weight: 0.05
obj_loss_weight: 0.7
loss_type: 'ComputeStudentMatchLoss'
ignore_thres_low: 0.1
ignore_thres_high: 0.6
uncertain_aug: True
use_ota: False
multi_label: False
ignore_obj: False
pseudo_label_with_obj: True
pseudo_label_with_bbox: True
pseudo_label_with_cls: False
with_da_loss: False
da_loss_weights: 0.01
epoch_adaptor: True
resample_high_percent: 0.25
resample_low_percent: 0.99
ema_rate: 0.999
cosine_ema: True
imitate_teacher: False
ssod_hyp:
with_gt: False
mosaic: 1.0
cutout: 0.5
autoaugment: 0.5
scale: 0.8
degrees: 0.0
shear: 0.0
# 开始半监督训练后，经过几个epoch所有指标变为0，请问是什么问题呢？