Comments (19)
BowieHsu
commented on August 16, 2024
@kingzcheung 您好，这个模型转出用的是最新的代码吗，有别的信息可以提供一下吗
from efficientteacher.
kingzcheung
commented on August 16, 2024
@BowieHsu 我更新了代码，反而出错了。
update : 抱歉，这是低级错误，把module 看成model了。不过模型转换出来依然有问题
convert_efficient_to_yolov5(
efficient_path='efficientteacher/runs/exp5/weights/best.pt',
yolov5_path='yolov5/object/train/exp2/weights/best.pt',
save_path='object.pt',
map_path="scripts/mula_convertor/map_v5.txt",
)from efficientteacher.
BowieHsu
commented on August 16, 2024
@kingzcheung 您好，运行是在efficientteacher/scripts/mula_convertor目录下操作的吗
from efficientteacher.
kingzcheung
commented on August 16, 2024
@kingzcheung 您好，运行是在efficientteacher/scripts/mula_convertor目录下操作的吗
我额外写的脚本调用，上面的一些path因为是隐私所以是虚假的，这个只要文件能加载到，应该不影响吧
from efficientteacher.
BowieHsu
commented on August 16, 2024
@kingzcheung 您好，这块我没确认过，有可能需要把调用的Path加载到python解释器中，因为这块的实现比较naive，需要convert_pt_to_efficient.py文件运行时能够抓到efficientteacher/scripts/mula_convertor/models/yolo.py的实现，能先尝试在目录下转换吗
from efficientteacher.
kingzcheung
commented on August 16, 2024
@kingzcheung 您好，这块我没确认过，有可能需要把调用的Path加载到python解释器中，因为这块的实现比较naive，需要convert_pt_to_efficient.py文件运行时能够抓到efficientteacher/scripts/mula_convertor/models/yolo.py的实现，能先尝试在目录下转换吗
我尝试了在 efficientteacher/scripts/mula_convertor 转换模型，确实没有出现问题，不过我使用yolov5 项目的detect.py 推理时还是没有识别到的问题，没有出现任何框选。
from efficientteacher.
BowieHsu
commented on August 16, 2024
@kingzcheung 您好，我来查一下，我们当前只测试过COCO的模型转出，所以可能是anchor不一致导致的，我这边重新调一下这个写法，还请您先查一下efficientteacher/runs/exp5/weights/best.pt和yolov5/object/train/exp2/weights/best.pt这两个模型的anchor一致吗
from efficientteacher.
kingzcheung
commented on August 16, 2024
@kingzcheung 您好，我来查一下，我们当前只测试过COCO的模型转出，所以可能是anchor不一致导致的，我这边重新调一下这个写法，还请您先查一下efficientteacher/runs/exp5/weights/best.pt和yolov5/object/train/exp2/weights/best.pt这两个模型的anchor一致吗
我这边做了检查，两边使用的都是coco的 anchor
from efficientteacher.
BowieHsu
commented on August 16, 2024
@kingzcheung 理论上来说不应该，您能打印一下两个模型输出的tensor之间的差异吗
from efficientteacher.
kingzcheung
commented on August 16, 2024
efficientteacher 模型转换到yolov5后 识别的tensor 打印点：
pred = model(im, augment=augment, visualize=visualize)
print(pred)
Fusing layers...
YOLOv5s summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs
tensor([[[6.87528e+00, 6.43499e+00, 2.00161e+01, 2.11257e+01, 1.54779e-04, 9.99998e-01],
[1.30909e+01, 6.93680e+00, 2.63801e+01, 2.21640e+01, 3.63503e-04, 9.99998e-01],
[1.96141e+01, 7.67334e+00, 3.29301e+01, 2.29983e+01, 4.53216e-04, 9.99998e-01],
...,
[5.59769e+02, 4.52719e+02, 2.05024e+02, 1.30795e+02, 1.45318e-03, 9.99998e-01],
[5.89868e+02, 4.52744e+02, 1.81657e+02, 1.27016e+02, 1.70840e-03, 9.99998e-01],
[6.19069e+02, 4.55649e+02, 1.75650e+02, 1.29385e+02, 7.19466e-04, 9.99998e-01]]], device='cuda:0')
在 efficientteacher 项目直接推理的打打印点：
pred = model(img, augment=augment)[0]
print(pred)
(tensor([[[6.60015e+00, 5.98306e+00, 1.32401e+01, 1.37972e+01, 3.96001e-06, 9.99998e-01],
[1.18214e+01, 6.99378e+00, 2.34359e+01, 1.47208e+01, 1.45329e-06, 9.99998e-01],
[1.70586e+01, 7.15639e+00, 3.26201e+01, 1.50374e+01, 4.93601e-07, 9.99998e-01],
...,
[5.52435e+02, 4.42294e+02, 1.82934e+02, 8.94892e+01, 2.06770e-06, 9.99998e-01],
[5.82577e+02, 4.42004e+02, 1.17093e+02, 8.49429e+01, 2.62460e-06, 9.99998e-01],
[6.13023e+02, 4.45002e+02, 1.16003e+02, 1.00126e+02, 1.26170e-06, 9.99998e-01]]], device='cuda:0'), [tensor([[[[[ 6.74497e-01, 5.06312e-01, 3.03624e-01, 6.04285e-02, -1.24393e+01, 1.33767e+01],
[-4.46533e-02, 7.86648e-01, 1.18273e+00, 1.28433e-01, -1.34417e+01, 1.33779e+01],
[-7.71460e-01, 8.34352e-01, 2.23161e+00, 1.51311e-01, -1.45215e+01, 1.33932e+01],
...,
[ 2.36848e-01, 1.13578e+00, 1.96370e+00, 1.91162e-01, -1.38027e+01, 1.34041e+01],
[-2.98665e-01, 9.14395e-01, 1.06673e+00, 2.00059e-01, -1.39895e+01, 1.33882e+01],
[-8.86837e-01, 3.92860e-01, 3.87051e-01, 2.64134e-01, -1.33691e+01, 1.33754e+01]],
[[ 8.75882e-01, 9.87823e-02, 4.57808e-01, 8.37938e-01, -1.31697e+01, 1.33821e+01],
[ 3.19443e-01, 3.06354e-01, 1.66718e+00, 9.36480e-01, -1.38163e+01, 1.33831e+01],
[-5.55783e-01, 5.07504e-01, 2.86765e+00, 1.02312e+00, -1.48052e+01, 1.33993e+01],
...,
[ 5.00089e-01, 1.12444e+00, 2.41781e+00, 1.19441e+00, -1.25122e+01, 1.34013e+01],
[-3.39014e-01, 1.64684e+00, 1.66342e+00, 1.43451e+00, -1.21144e+01, 1.33884e+01],
[-1.21200e+00, 8.14139e-01, 5.66407e-01, 1.23168e+00, -1.20300e+01, 1.33729e+01]],
[[ 6.76698e-01, -1.28984e-01, 3.82106e-01, 1.76479e+00, -1.43450e+01, 1.33873e+01],
[ 3.11877e-01, 1.92059e-01, 1.59658e+00, 1.94928e+00, -1.45286e+01, 1.33844e+01],
[-6.06724e-01, 3.71015e-01, 2.78588e+00, 2.10980e+00, -1.56691e+01, 1.33950e+01],
...,
[ 8.08078e-01, 3.73996e-01, 2.26209e+00, 2.05011e+00, -1.09903e+01, 1.33812e+01],
[-2.48257e-01, 3.48976e-01, 1.50234e+00, 2.11198e+00, -1.10524e+01, 1.33732e+01],
[-1.53467e+00, 8.33828e-02, 6.83295e-01, 1.94420e+00, -1.11933e+01, 1.33599e+01]],
...,
[[ 4.99498e-01, 1.21063e-01, 2.94229e-01, 1.66222e+00, -1.32318e+01, 1.33897e+01],
[ 1.50584e-01, 3.70697e-02, 1.55339e+00, 1.77825e+00, -1.38844e+01, 1.33905e+01],
[-4.07494e-01, 2.82368e-01, 2.79688e+00, 1.65930e+00, -1.38822e+01, 1.34080e+01],
...,
[ 1.88082e-01, -2.09035e-01, 2.87354e+00, 1.96797e+00, -1.36774e+01, 1.34089e+01],
[-2.46546e-01, -2.04200e-01, 1.52666e+00, 1.96117e+00, -1.37466e+01, 1.34000e+01],
[-9.69013e-01, -2.30853e-02, 5.71843e-01, 1.76493e+00, -1.32071e+01, 1.33817e+01]],
[[ 5.36883e-01, -2.00774e-01, 3.54404e-01, 7.06968e-01, -1.39745e+01, 1.33957e+01],
[ 5.47810e-01, -1.82674e-01, 1.85752e+00, 8.17104e-01, -1.39656e+01, 1.33875e+01],
[-3.61819e-01, 2.44233e-02, 2.87981e+00, 7.18734e-01, -1.35277e+01, 1.33961e+01],
...,
[ 1.17976e-01, -2.97486e-01, 2.74687e+00, 9.04568e-01, -1.38092e+01, 1.34059e+01],
[-3.08595e-01, -4.89858e-01, 1.53749e+00, 9.91627e-01, -1.31641e+01, 1.33971e+01],
[-9.72284e-01, -3.53114e-01, 5.50531e-01, 7.89502e-01, -1.30059e+01, 1.33786e+01]],
[[ 2.07921e-01, -3.46017e-01, 2.69398e-01, -9.32622e-03, -1.34938e+01, 1.33915e+01],
[ 2.54994e-01, -5.04227e-01, 1.51233e+00, 8.99568e-02, -1.34424e+01, 1.33831e+01],
[-3.86826e-01, -6.37741e-01, 2.52690e+00, 1.48303e-01, -1.30858e+01, 1.33823e+01],
...,
[ 1.92794e-01, -5.67446e-01, 2.36635e+00, 1.22246e-01, -1.32405e+01, 1.33902e+01],
[-3.21186e-01, -5.21928e-01, 1.29335e+00, 1.10732e-01, -1.31409e+01, 1.33903e+01],
[-6.80707e-01, -4.49711e-01, 4.37580e-01, 5.28771e-02, -1.28455e+01, 1.33746e+01]]],
[[[ 7.48402e-01, 5.11306e-01, -6.81176e-02, -6.38693e-01, -1.31001e+01, 1.34214e+01],
[ 7.36565e-02, 8.27112e-01, 5.45987e-01, -6.24792e-01, -1.43219e+01, 1.34269e+01],
[-2.41651e-01, 8.70819e-01, 1.29144e+00, -6.32463e-01, -1.46476e+01, 1.34170e+01],
...,
[ 9.67242e-02, 1.13591e+00, 1.13470e+00, -5.40473e-01, -1.41200e+01, 1.34056e+01],
[-2.99780e-01, 9.14259e-01, 5.04766e-01, -6.00412e-01, -1.47976e+01, 1.34141e+01],
[-8.08824e-01, 3.74760e-01, 1.73384e-03, -4.54236e-01, -1.38590e+01, 1.34240e+01]],
[[ 9.54107e-01, 1.18947e-01, -2.32301e-02, -1.11158e-01, -1.37242e+01, 1.34233e+01],
[ 4.16177e-01, 3.50682e-01, 7.07961e-01, -4.95587e-02, -1.45059e+01, 1.34214e+01],
[ 4.52525e-02, 5.55174e-01, 1.36992e+00, -5.09930e-02, -1.43411e+01, 1.34140e+01],
...,
[ 2.73435e-01, 1.15232e+00, 1.11658e+00, 4.39565e-02, -1.25526e+01, 1.33982e+01],
[-4.18873e-01, 1.71816e+00, 7.13106e-01, 1.00026e-01, -1.25475e+01, 1.33890e+01],
[-1.18834e+00, 8.28616e-01, 4.51747e-02, 8.11021e-02, -1.22471e+01, 1.34084e+01]],
[[ 7.71482e-01, -6.53934e-02, -6.28459e-02, 3.57317e-01, -1.46281e+01, 1.34295e+01],
[ 4.09730e-01, 2.75445e-01, 6.61864e-01, 3.90840e-01, -1.49590e+01, 1.34277e+01],
[ 7.72877e-02, 5.05446e-01, 1.33445e+00, 3.94075e-01, -1.50815e+01, 1.34294e+01],
...,
[ 6.30887e-01, 4.42767e-01, 1.01142e+00, 3.85424e-01, -1.08615e+01, 1.34020e+01],
[-3.06490e-01, 4.27147e-01, 6.07101e-01, 3.67796e-01, -1.14805e+01, 1.33992e+01],
[-1.50136e+00, 1.58030e-01, 1.05110e-01, 3.52670e-01, -1.13714e+01, 1.34134e+01]],
...,
[[ 5.83077e-01, 8.61396e-02, -1.22040e-01, 3.31043e-01, -1.34316e+01, 1.34262e+01],
[ 2.86279e-01, 6.90010e-02, 6.66190e-01, 3.19920e-01, -1.45196e+01, 1.34283e+01],
[ 1.97875e-01, 2.88903e-01, 1.40270e+00, 2.44913e-01, -1.32817e+01, 1.34206e+01],
...,
[-1.04524e-01, -2.26355e-01, 1.38110e+00, 3.97536e-01, -1.31353e+01, 1.34040e+01],
[-3.21323e-01, -2.14526e-01, 6.73358e-01, 3.80775e-01, -1.43316e+01, 1.34064e+01],
[-9.51475e-01, -5.44854e-02, 4.10319e-02, 4.34671e-01, -1.34381e+01, 1.34161e+01]],
[[ 6.90916e-01, -2.30640e-01, -5.41114e-02, -1.30981e-01, -1.46829e+01, 1.34240e+01],
[ 7.62853e-01, -1.52673e-01, 8.41797e-01, -1.15791e-01, -1.48526e+01, 1.34218e+01],
[ 2.14220e-01, 4.29813e-02, 1.41404e+00, -2.01148e-01, -1.31553e+01, 1.34106e+01],
...,
[-1.23392e-01, -2.88000e-01, 1.31196e+00, -1.01647e-01, -1.38644e+01, 1.33983e+01],
[-3.35310e-01, -4.88751e-01, 6.58192e-01, -3.49770e-02, -1.40765e+01, 1.33967e+01],
[-9.20721e-01, -3.63649e-01, 2.06459e-02, -4.14176e-02, -1.37055e+01, 1.34128e+01]],
[[ 3.46918e-01, -3.63900e-01, -9.31104e-02, -6.11638e-01, -1.43081e+01, 1.34174e+01],
[ 5.12903e-01, -5.11441e-01, 7.26547e-01, -6.07360e-01, -1.42990e+01, 1.34151e+01],
[ 7.16262e-02, -6.50195e-01, 1.45742e+00, -5.90442e-01, -1.30807e+01, 1.34011e+01],
...,
[ 1.34119e-01, -5.84508e-01, 1.33573e+00, -6.16746e-01, -1.34098e+01, 1.34020e+01],
[-2.71122e-01, -5.29359e-01, 6.10607e-01, -5.99055e-01, -1.40611e+01, 1.34014e+01],
[-6.15004e-01, -4.36942e-01, 1.70729e-02, -5.33553e-01, -1.36644e+01, 1.34092e+01]]],
[[[ 8.08648e-01, 6.29964e-01, -5.96452e-01, -4.29293e-01, -1.30372e+01, 1.33479e+01],
[ 1.76828e-03, 9.13490e-01, -1.53650e-01, -4.10489e-01, -1.42348e+01, 1.33378e+01],
[-1.60185e-01, 9.94883e-01, 2.53160e-01, -4.03873e-01, -1.46466e+01, 1.33288e+01],
...,
[ 9.61155e-02, 1.27130e+00, 1.96904e-01, -3.08372e-01, -1.38787e+01, 1.33373e+01],
[-2.68666e-01, 1.09823e+00, -1.65613e-01, -3.36964e-01, -1.47421e+01, 1.33390e+01],
[-8.05162e-01, 5.81060e-01, -5.32642e-01, -2.54309e-01, -1.42942e+01, 1.33314e+01]],
[[ 1.07321e+00, 2.52791e-01, -5.98803e-01, 1.57982e-01, -1.38542e+01, 1.33538e+01],
[ 4.18910e-01, 4.22958e-01, -1.09949e-01, 2.11779e-01, -1.46982e+01, 1.33361e+01],
[ 1.69676e-01, 6.90273e-01, 2.47906e-01, 2.40175e-01, -1.48897e+01, 1.33267e+01],
...,
[ 3.25819e-01, 1.25041e+00, 1.20947e-01, 3.50174e-01, -1.27702e+01, 1.33350e+01],
[-4.11582e-01, 1.85554e+00, -1.60766e-01, 4.51898e-01, -1.29139e+01, 1.33410e+01],
[-1.18619e+00, 1.08253e+00, -5.72207e-01, 3.64236e-01, -1.26802e+01, 1.33300e+01]],
[[ 9.00905e-01, 2.32686e-02, -6.06810e-01, 6.69494e-01, -1.49321e+01, 1.33570e+01],
[ 3.90712e-01, 3.66619e-01, -1.32089e-01, 7.29236e-01, -1.50689e+01, 1.33433e+01],
[ 2.08342e-01, 5.55341e-01, 2.54168e-01, 7.71537e-01, -1.55508e+01, 1.33401e+01],
...,
[ 6.30006e-01, 4.35883e-01, 7.24195e-02, 7.57492e-01, -1.08914e+01, 1.33406e+01],
[-3.27134e-01, 4.21619e-01, -1.83846e-01, 7.59332e-01, -1.16202e+01, 1.33330e+01],
[-1.49545e+00, 2.58331e-01, -5.13876e-01, 7.09674e-01, -1.16361e+01, 1.33237e+01]],
...,
[[ 6.87042e-01, 1.67528e-01, -6.57782e-01, 6.04691e-01, -1.36502e+01, 1.33512e+01],
[ 2.77209e-01, 7.75030e-02, -1.28606e-01, 6.38182e-01, -1.46196e+01, 1.33303e+01],
[ 2.34680e-01, 3.33586e-01, 2.81277e-01, 6.06537e-01, -1.36384e+01, 1.33176e+01],
...,
[-1.73763e-01, -2.09749e-01, 2.46081e-01, 7.51963e-01, -1.32353e+01, 1.33243e+01],
[-3.18626e-01, -1.82266e-01, -1.22568e-01, 7.32852e-01, -1.44829e+01, 1.33250e+01],
[-9.40674e-01, 3.59760e-02, -5.50374e-01, 6.63052e-01, -1.37502e+01, 1.33238e+01]],
[[ 7.86925e-01, -1.15625e-01, -5.82386e-01, 1.14343e-01, -1.48294e+01, 1.33564e+01],
[ 6.88018e-01, -9.64651e-02, 8.66806e-03, 1.57381e-01, -1.48683e+01, 1.33355e+01],
[ 2.70731e-01, 9.05364e-02, 3.11937e-01, 6.77392e-02, -1.34322e+01, 1.33188e+01],
...,
[-1.72837e-01, -2.62251e-01, 2.48980e-01, 1.77793e-01, -1.38429e+01, 1.33267e+01],
[-3.28792e-01, -4.57398e-01, -1.28541e-01, 2.44772e-01, -1.42099e+01, 1.33228e+01],
[-9.21992e-01, -2.98060e-01, -5.57187e-01, 1.44136e-01, -1.40311e+01, 1.33229e+01]],
[[ 3.66557e-01, -3.47312e-01, -5.81334e-01, -4.47403e-01, -1.44710e+01, 1.33602e+01],
[ 3.74931e-01, -5.48204e-01, -8.08748e-03, -4.19967e-01, -1.42788e+01, 1.33497e+01],
[ 4.38403e-02, -6.81130e-01, 3.56392e-01, -3.88980e-01, -1.31444e+01, 1.33293e+01],
...,
[ 2.93544e-02, -6.17740e-01, 3.05559e-01, -4.17952e-01, -1.32600e+01, 1.33378e+01],
[-3.13902e-01, -5.62941e-01, -1.20770e-01, -4.27025e-01, -1.40977e+01, 1.33373e+01],
[-6.75273e-01, -4.45140e-01, -5.08624e-01, -3.99384e-01, -1.38650e+01, 1.33343e+01]]]]], device='cuda:0'), tensor([[[[[ 4.72216e-01, 5.43858e-01, -1.35079e-01, -6.18792e-01, -1.29435e+01, 1.33558e+01],
[ 1.55772e-01, 7.67091e-01, 5.30099e-01, -6.72633e-01, -1.22630e+01, 1.33364e+01],
[ 6.82467e-02, 7.99657e-01, 1.45183e+00, -6.35263e-01, -1.21926e+01, 1.33426e+01],
...,
[ 1.08158e-01, 6.39753e-01, 1.20708e+00, -6.71171e-01, -1.12328e+01, 1.33488e+01],
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[ 6.04604e-01, 2.26257e-01, -6.15817e-01, -5.40172e-01, -9.13536e+00, 1.33202e+01],
[-6.40930e-01, 3.40539e-01, -9.17356e-01, -5.30384e-01, -9.12940e+00, 1.33194e+01]],
[[ 5.45346e-01, 3.41146e-01, -9.22098e-01, -1.33981e-01, -1.15424e+01, 1.33206e+01],
[-6.79618e-02, 1.33717e-01, -4.59864e-01, -1.72448e-01, -1.09833e+01, 1.33178e+01],
[ 1.12484e-01, 7.36278e-02, 4.72003e-02, -1.92556e-01, -1.02103e+01, 1.33189e+01],
...,
[ 3.22947e-01, 5.21531e-01, -5.54726e-02, -1.16325e-01, -9.87925e+00, 1.33181e+01],
[ 3.48705e-01, 1.79402e-01, -5.44470e-01, -1.52572e-01, -9.05788e+00, 1.33208e+01],
[-6.25063e-01, 3.36431e-02, -9.08750e-01, -1.72644e-01, -9.02823e+00, 1.33190e+01]],
...,
[[ 6.76672e-01, -4.89965e-01, -8.91540e-01, -7.66430e-02, -1.11852e+01, 1.33155e+01],
[-8.22401e-02, -3.23369e-01, -4.74051e-01, -1.54331e-01, -1.11213e+01, 1.33161e+01],
[-8.47083e-02, -2.80078e-02, -1.22333e-03, -2.31550e-01, -1.16001e+01, 1.33190e+01],
...,
[ 4.83952e-01, 2.76811e-01, -9.09394e-02, -2.80274e-01, -1.11921e+01, 1.33158e+01],
[ 6.65498e-01, 3.46284e-01, -6.48380e-01, -2.80631e-01, -1.02695e+01, 1.33157e+01],
[-6.75376e-01, -9.98554e-02, -9.05780e-01, -1.85998e-01, -1.05056e+01, 1.33134e+01]],
[[ 7.22838e-01, -2.52498e-01, -8.75496e-01, -5.89726e-01, -1.16722e+01, 1.33179e+01],
[-2.86920e-02, 9.55018e-02, -4.37307e-01, -6.39902e-01, -1.16675e+01, 1.33219e+01],
[-2.47382e-02, 2.11844e-01, 2.14256e-02, -6.95075e-01, -1.16027e+01, 1.33234e+01],
...,
[ 1.87333e-01, 1.29277e-01, -4.00531e-02, -6.54766e-01, -1.13033e+01, 1.33193e+01],
[ 2.39309e-01, 6.91752e-02, -4.95763e-01, -6.20946e-01, -1.09299e+01, 1.33196e+01],
[-9.77140e-01, -7.43380e-02, -8.02608e-01, -6.08092e-01, -1.07169e+01, 1.33122e+01]],
[[ 6.37578e-01, -1.05116e+00, -7.75793e-01, -9.06350e-01, -1.20876e+01, 1.33155e+01],
[ 9.23427e-02, -1.12392e+00, -3.63190e-01, -9.36672e-01, -1.22721e+01, 1.33206e+01],
[-5.68643e-02, -1.04768e+00, 1.06809e-01, -9.59471e-01, -1.20333e+01, 1.33239e+01],
...,
[-2.70016e-01, -9.86414e-01, 1.31132e-01, -9.60594e-01, -1.26621e+01, 1.33159e+01],
[ 1.20839e-01, -1.06165e+00, -4.66756e-01, -9.37714e-01, -1.18377e+01, 1.33161e+01],
[-8.43962e-01, -1.03846e+00, -8.30006e-01, -9.39658e-01, -1.17768e+01, 1.33093e+01]]],
[[[ 6.38975e-01, 1.87508e+00, -8.70804e-01, -8.88702e-01, -1.38243e+01, 1.32983e+01],
[ 7.48581e-01, 2.15857e+00, -8.98819e-01, -1.03910e+00, -1.32222e+01, 1.33078e+01],
[ 1.86536e-01, 2.19013e+00, -6.79530e-01, -1.04397e+00, -1.21638e+01, 1.33088e+01],
...,
[-7.44559e-02, 1.91955e+00, -6.56773e-01, -9.68647e-01, -1.12322e+01, 1.33089e+01],
[-7.46219e-01, 1.90018e+00, -8.95342e-01, -9.55949e-01, -1.15174e+01, 1.33124e+01],
[-5.19046e-01, 2.16434e+00, -9.26320e-01, -8.87699e-01, -1.23607e+01, 1.33077e+01]],
[[ 1.02696e+00, 6.18900e-01, -9.90005e-01, -7.93986e-01, -1.37749e+01, 1.33028e+01],
[ 7.49289e-01, 3.83027e-01, -9.25755e-01, -8.97340e-01, -1.24529e+01, 1.33098e+01],
[ 3.16335e-01, 4.88311e-01, -6.72367e-01, -8.86729e-01, -1.09404e+01, 1.33127e+01],
...,
[ 5.36433e-02, 6.52589e-01, -7.23235e-01, -8.37765e-01, -9.96042e+00, 1.33141e+01],
[-6.28480e-01, 5.84445e-01, -9.61043e-01, -8.43327e-01, -1.03800e+01, 1.33175e+01],
[-7.53711e-01, 7.53406e-01, -1.02731e+00, -7.80081e-01, -1.17605e+01, 1.33070e+01]],
[[ 9.28128e-01, 3.75442e-01, -9.71116e-01, -5.05561e-01, -1.29628e+01, 1.33024e+01],
[ 7.91030e-01, 1.23571e-01, -9.33399e-01, -6.15394e-01, -1.14039e+01, 1.33071e+01],
[ 2.07807e-01, 1.14824e-01, -6.90793e-01, -6.15640e-01, -9.88643e+00, 1.33089e+01],
...,
[ 1.12211e-01, 5.23475e-01, -7.51137e-01, -5.33085e-01, -9.65023e+00, 1.33101e+01],
[-7.65204e-01, 2.62170e-01, -9.54702e-01, -5.57220e-01, -1.01156e+01, 1.33132e+01],
[-8.71314e-01, 1.32432e-01, -1.03781e+00, -5.44917e-01, -1.18423e+01, 1.33031e+01]],
...,
[[ 9.99298e-01, -5.42530e-01, -9.93837e-01, -4.54847e-01, -1.25085e+01, 1.33031e+01],
[ 9.08202e-01, -4.04805e-01, -9.11695e-01, -5.91552e-01, -1.16535e+01, 1.33051e+01],
[ 1.11490e-01, -1.31235e-01, -6.99337e-01, -6.28153e-01, -1.13025e+01, 1.33077e+01],
...,
[ 1.44398e-01, 1.58990e-01, -7.36581e-01, -6.78295e-01, -1.10320e+01, 1.33165e+01],
[-4.79264e-01, 2.20836e-01, -1.01278e+00, -6.79628e-01, -1.10996e+01, 1.33161e+01],
[-8.93172e-01, -2.56855e-01, -1.01275e+00, -5.62808e-01, -1.22121e+01, 1.33005e+01]],
[[ 1.09029e+00, -7.35472e-01, -1.00489e+00, -7.81400e-01, -1.32111e+01, 1.32958e+01],
[ 8.91565e-01, -4.61845e-01, -9.17022e-01, -9.06211e-01, -1.23728e+01, 1.33054e+01],
[ 1.90415e-01, -3.79011e-01, -7.13032e-01, -9.17656e-01, -1.17992e+01, 1.33094e+01],
...,
[-7.04294e-02, -2.95519e-01, -7.57935e-01, -9.38140e-01, -1.14207e+01, 1.33118e+01],
[-5.25382e-01, -3.51844e-01, -1.02587e+00, -9.23466e-01, -1.17065e+01, 1.33110e+01],
[-1.16219e+00, -5.96589e-01, -1.07364e+00, -8.70706e-01, -1.26133e+01, 1.32945e+01]],
[[ 8.41249e-01, -1.23941e+00, -8.79113e-01, -9.16017e-01, -1.34382e+01, 1.32932e+01],
[ 9.53170e-01, -1.74460e+00, -8.65154e-01, -1.05151e+00, -1.32494e+01, 1.33029e+01],
[ 2.73961e-01, -1.68873e+00, -6.58056e-01, -1.03968e+00, -1.27626e+01, 1.33036e+01],
...,
[-4.81934e-01, -1.65191e+00, -6.18348e-01, -1.03577e+00, -1.30891e+01, 1.32990e+01],
[-6.06879e-01, -1.68593e+00, -9.43750e-01, -1.07093e+00, -1.28506e+01, 1.33038e+01],
[-7.15065e-01, -1.36673e+00, -9.50239e-01, -9.58898e-01, -1.35831e+01, 1.32921e+01]]]]], device='cuda:0')])
480x640 Done. (0.067s)
runs/detect/exp5/1_cut6.jpg
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BowieHsu
commented on August 16, 2024
@kingzcheung 您好，我这边已经复现出现转换后的模型输出不一致的问题了，正在解决
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BowieHsu
commented on August 16, 2024
@kingzcheung 您好，试试我们最新的转出代码
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kingzcheung
commented on August 16, 2024
我测试好像不行，不如我给你我的模型？
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kingzcheung
commented on August 16, 2024
我已经发送到你的邮件提供给您debug~
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BowieHsu
commented on August 16, 2024
好的，已收到，我测试一下
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BowieHsu
commented on August 16, 2024
是这样的结果吗，我用转出的模型在yolov5 7.0代码上推出来的，好像names不对，您yaml里的names修改过吗
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kingzcheung
commented on August 16, 2024
我的结果是完全没有框，不过我 的yolo版本和你的不一样，我保持一致看看
update： 依然转出来的模型无法识别
我已经私发给你我转换后的模型以及调用方式。
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BowieHsu
commented on August 16, 2024
您好，git pull我们最新的代码应该是能导出然后用标准YOLOv5运行的，您先试试呢
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kingzcheung
commented on August 16, 2024
您好，git pull我们最新的代码应该是能导出然后用标准YOLOv5运行的，您先试试呢
已解决，问题出现在 yolov5_path 参数上， yolov5_path 的模型必须是 ultralytics/yolov5 原版模型,而不能是自己在 ultralytics/yolov5 基础上训练的模型。这个我建议加上一些注释避免其他人碰到这个坑~
感谢作者的付出
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Related Issues (20)
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