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Thanks for the amazing work
I am having issue regarding network learning. My NASNet model ain't learning. Training accuracy is improving but validation accuracy isn't changing and stuck at 0.4194.
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### Search before asking
- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no simi…
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Hi，
I read your whole code and think it’s a excellect work
But I still have some doubts, can you help me with some questions?
From what I understand, detection network generate proposal regi…
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Hi @anandhu-eng and @arjunsuresh,
I just tried the CM-MLPerf workflow for ResNet benchmark with the latest CM4MLOps mlperf-inference branch on my Windows machine and it fails when downloading a mod…
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## Keyword: super resolution
There is no result
## Keyword: gan
### Towards Discovery and Attribution of Open-world GAN Generated Images
- **Authors:** Sharath Girish, Saksham Suri, Saketh Rambhatla…
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Can this be done by leveraging the onnxruntime work we already have as a back end?
As a preliminary step, learn to add a Cuda back end,
then change it to MIGraphX/ROCm
See [https://github.com…
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Hi,thank you for your great work!I found that the novel category names are already included in the generated captioning data.For example,"a image of sofa" and "a image of toilet".So does this mean tha…
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:red_circle: Project Title: Sugarcane Leaf Disease Detection
:red_circle: Aim: To develop a machine learning model capable of classifying sugarcane leaves into three categories: Healthy, Red Rot, a…
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I want to recreate the project of Resnet50 and Imagenet, and follow the instructions of readme.md (https://github.com/mlcommons/training/blob/master/image_classification/README.md), I get the followin…