When I use command like below
python detect.py --source ./data/images/ --image-size 320
I can get all right results.
But when I use torch.hub.load method
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
list_img = glob("./yolov5/data/images/*.jpg")
results = model(list_img, 320)
Only first image can be predicted.
Even I changed the data source, I faced same situation.
This seems a customer app issue.
Have you checked with the author if the code support batch inference?
Yes , I can use same function on my laptop with 1080ti.
Could you check if the package between Nano and desktop is identical or not first?
More, may I know how you install the PyTorch?
Do you use our prebuilt shared in the below topic?
Below are pre-built PyTorch pip wheel installers for Python on Jetson Nano, Jetson TX1/TX2, Jetson Xavier NX/AGX, and Jetson AGX Orin with JetPack 4.2 and newer.
Download one of the PyTorch binaries from below for your version of JetPack, and see the installation instructions to run on your Jetson. These pip wheels are built for ARM aarch64 architecture, so run these commands on your Jetson (not on a host PC).
PyTorch pip wheels
PyTorch v1.12.0JetPack 5.0 (L4T R34.1.0) / JetPack 5.0.1 (L4T …
I use the torch.whl file you provided and also follow the instrction of installation.
If “package” you mentioned is pytorch, I have already tried version from 1.7 ~1.10 on Jetson Nano.
Or if “package” you mentioned is yolov5, I use same latest version.
(Btw, it is torch 1.7.0 on my desktop.)
Could you try on your board for reproducing this situation?
Just want to confirm first.
Do you get the expected output on a desktop environment with PyTorch?
If yes, which version do you use for desktop?
It seems like the outputs is correct.
And as you can see, the pytorch version is 1.7.0.
There is no update from you for a period, assuming this is not an issue any more.
Hence we are closing this topic. If need further support, please open a new one.
The screenshot you shared above can detect two images correctly.
Do you run it on Nano?
I can not reproduce same results on nano
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