mAP using osnet_x1_0 and resnet50 is weird

[a2082761]

Hello,
I just runned Get started code as below using pretrained model 'osnet_x1_0' and even 'resnet50' too.
However, the result was weird. mAP was just 3.9%.... and when I used resnet50, it was 2.xx%.
How can I increase mAP and can you check my source code which I just want to use pretrained model and I don't want to train dadaset.
Thanks.

** Results **
mAP: 3.9%
CMC curve
Rank-1 : 13.2%
Rank-5 : 25.9%
Rank-10 : 33.3%
Rank-20 : 40.3%

query: 3368

gallery 15913

`import torchreid
import torch

datamanager = torchreid.data.ImageDataManager(
root="reid-data",
sources="market1501",
targets="market1501",
height=256,
width=128,
batch_size_train=32,
batch_size_test=100,
transforms=["random_flip", "random_crop"]
)

model = torchreid.models.build_model(
name="osnet_x1_0",
num_classes=datamanager.num_train_pids,
loss="softmax",
pretrained=True
)

model = model.cuda()

optimizer = torchreid.optim.build_optimizer(
model,
optim="adam",
lr=0.0003
)

scheduler = torchreid.optim.build_lr_scheduler(
optimizer,
lr_scheduler="single_step",
stepsize=20
)

engine = torchreid.engine.ImageSoftmaxEngine(
datamanager,
model,
optimizer=optimizer,
scheduler=scheduler,
label_smooth=True
)

engine.run(
save_dir="log/osnet_x1_0",
max_epoch=60,
eval_freq=10,
print_freq=10,
test_only=True,
visrank=True,
visrank_topk=10
)

`

[erictan23]

I got the same results as you, but i think you could try load other weights to get better mAP

[QiqLiang]

Hi, I got the same problem when validating my models, have you solved it?