Replies: 46 comments 8 replies
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Hoping to Use: CenterNet, https://openaccess.thecvf.com/content_ICCV_2019/html/Duan_CenterNet_Keypoint_Triplets_for_Object_Detection_ICCV_2019_paper.html |
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Thanks @surefyyq ! Hope to take a look soon. Please feel to reach out if you have other suggestions as well. |
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You can refer to this repo: https://github.com/nanmi/CenterNet-trt |
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I hope to use CenterPoint (CVPR paper) |
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I want to deploy a customed yolov5m.pt on jetson NX. |
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Bert !!!!! |
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Hoping to Use: YOLOP SLOWFAST SLOWONLY ST-GCN YOLOV5 DEEPSORT |
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Hope to use: DeepLabv3+ |
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Write codes in C++ API |
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@maronuu has made some progress with YoloR and described it in #565. Now it seems the |
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hoping for: torch.nn.ReflectionPad2d(), which is a padding implemention from pytorch. thx |
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Hoping to use Spatial Transformer Networks : link |
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Hoping to use DSFD Official Link |
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Hoping to Use: DeTR, https://github.com/facebookresearch/detr |
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Hoping to Use: Detectron2, https://github.com/facebookresearch/detectron2 We have had trouble with other routes to production, so we are taking a look at TensorRT. |
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Hoping to Use: yolact |
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Hoping to use DeiT variant of VIT |
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Hoping to Use: YOLOP SLOWFAST SLOWONLY ST-GCN YOLOV5 DEEPSORT |
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mobilenetv2 Please :) |
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I also have to convert ssd mobile net to tensorrt model for inferencing on jetson tx1 |
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StyleGAN2-pytorch Danke! |
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Hoping to use ByteTrack https://github.com/ifzhang/ByteTrack I was wondering if I have 100 Jetson AGX devices with the exact same specification, then do you I need to run torch2trt on each of these devices or can I run it on one device and use the output model.engine on the other devices for best performance. |
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Hoping to use ViTPose with Jetson AGX Orin: https://github.com/ViTAE-Transformer/ViTPose I used it's PyTorch to ONNX conversion code and then use trtexec to convert to an engine file, But the output heat map is different when using tensorRT inference, |
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please take more net detials，or the net paper！
…---Original---
From: ***@***.***>
Date: Wed, Oct 12, 2022 13:59 PM
To: ***@***.***>;
Cc: ***@***.******@***.***>;
Subject: Re: [NVIDIA-AI-IOT/torch2trt] What models are you using, or hoping touse, with TensorRT? (Discussion #531)
Hoping to use ViTPose with Jetson AGX Orin: https://github.com/ViTAE-Transformer/ViTPose
I used it's PyTorch to ONNX conversion code and then use trtexec to convert to an engine file, But the output heat map is different when using tensorRT inference,
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Take it easy
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From: ***@***.***>
Date: Wed, Nov 9, 2022 15:52 PM
To: ***@***.***>;
Cc: ***@***.******@***.***>;
Subject: Re: [NVIDIA-AI-IOT/torch2trt] What models are you using, or hoping to use, with TensorRT? (Discussion #531)
YOLOv7-pose
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code link
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Date: Wed, Nov 9, 2022 15:52 PM
To: ***@***.***>;
Cc: ***@***.******@***.***>;
Subject: Re: [NVIDIA-AI-IOT/torch2trt] What models are you using, or hoping to use, with TensorRT? (Discussion #531)
YOLOv7-pose
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Hoping to Use: Detic |
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please info the net details!
…---Original---
From: "Le Minh ***@***.***>
Date: Mon, Feb 6, 2023 12:38 PM
To: ***@***.***>;
Cc: ***@***.******@***.***>;
Subject: Re: [NVIDIA-AI-IOT/torch2trt] What models are you using, or hoping touse, with TensorRT? (Discussion #531)
Hoping to Use: Detic
https://github.com/facebookresearch/Detic
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Hoping to Use: FIDTM |
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Hoping to use: MiDaS |
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Hoping to use: Bot-Sort |
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Hoping to use: Yolov8 detect |
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Hoping to Use MiDaS https://pytorch.org/hub/intelisl_midas_v2/ |
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yolov8-pose |
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@lofyol Hi, you might want to keep an eye on this repo:
https://github.com/marcoslucianops/DeepStream-Yolo-Pose
The author mentioned lately (on Linkedin at least) that he will start
working on Yolo-Pose
Good Luck
…On Mon, 11 Sep 2023 at 09:57, lofyol ***@***.***> wrote:
yolov8-pose
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How to convert a model that has not supported operations? Is there any example for it? Documentation is quite hard to understand... |
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and others
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Hi All,
We're interested in hearing how you're using, or hoping to use torch2trt. This helps us prioritize features, or suggest workarounds if they exist. Please feel free to share here with
Using: <model name>, <model URL>
Notes: ...
or
Hoping to Use: <model name>, <model URL>
Notes: ...
If you find a model in this thread that matches you're use case, simply +1 that comment.
Look forward to hearing from you!
Best,
John
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