Pytorch Hub inference returns different results than detect.py

[Aoobi]

Hi,
I trained Yolov5x on my custom dataset using this notebook from roboflow: https://colab.research.google.com/drive/1gDZ2xcTOgR39tGGs-EZ6i3RTs16wmzZQ
In the notebook when I do inference I am getting good results, with confidence about 0.8 for basically every image, but when i downloaded the weights and used them in the hub locally, the results are different - if I set the threshold for 0.4 I am getting almost no results.
Also, the class names are not displayed when inferring with torchhub. I am using this code to run inference with pytorch hub:

model = torch.hub.load('ultralytics/yolov5', 'custom', path_or_model="path_to_my_weight_file.pt")
model.conf = 0.4
results = model(list_of_paths_to_my_images)
results.save("path_to_save_files")

Thanks

[github-actions]

👋 Hello @Aoobi, thank you for your interest in 🚀 YOLOv5! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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[glenn-jocher]

@Aoobi we don't support 3rd party training tools. If you'd like to train and deploy correctly simply follow the official Custom Training Tutorial and the Pytorch Hub Tutorials below, and if you'd like a notebook environment we recommend the notebooks below. Good luck!

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• Google Colab and Kaggle notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

[github-actions]

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