yxl23

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httpx.ConnectError: [WinError 10061] 由于目标计算机积极拒绝，无法连接。

What is Ollama's apikey and how can I check it?

httpx.ConnectError: [WinError 10061] 由于目标计算机积极拒绝，无法连接。

yes，thankyou

paddleocr4 onnx模型转engine出现错误咋解决啊

那我应该指定的维度是多少啊

Hello author, while using OBB to train my dataset, I found that the tilted boxes in the validation set images and training set images after training did not match. There were cases where the angles were skewed, and during my inference, the detection boxes were completely different from what I labeled

![image](https://github.com/user-attachments/assets/ca72511f-2617-44fb-abd1-17b81570436b)

Hello author, while using OBB to train my dataset, I found that the tilted boxes in the validation set images and training set images after training did not match. There were cases where the angles were skewed, and during my inference, the detection boxes were completely different from what I labeled

(yolov10-main) D:\shenduxuexi\yolov8>yolo train model=yolov8s-obb.pt data=D:\shenduxuexi\yolov8\ultralytics\cfg\datasets\DOTAv2.yaml epochs=10

Hello author, while using OBB to train my dataset, I found that the tilted boxes in the validation set images and training set images after training did not match. There were cases where the angles were skewed, and during my inference, the detection boxes were completely different from what I labeled

![image](https://github.com/user-attachments/assets/faa894ea-7d12-40dd-b18f-dfa3c1adca8c)

Hello author, while using OBB to train my dataset, I found that the tilted boxes in the validation set images and training set images after training did not match. There were cases where the angles were skewed, and during my inference, the detection boxes were completely different from what I labeled

I have tried all the methods that should have been attempted, but still cannot ![image](https://github.com/user-attachments/assets/2933eba6-d85f-43af-a438-2ff8881015da)

Hello author, while using OBB to train my dataset, I found that the tilted boxes in the validation set images and training set images after training did not match. There were cases where the angles were skewed, and during my inference, the detection boxes were completely different from what I labeled

This is my dataset category label 0 0.446020 0.206658 0.868090 0.136735 0.914797 0.771074 0.492727 0.840998 0 0.013158 0.186404 0.459795 0.186404 0.459795 0.837719 0.013158 0.837719 But if there is a problem...

Hello author, while using OBB to train my dataset, I found that the tilted boxes in the validation set images and training set images after training did not match. There were cases where the angles were skewed, and during my inference, the detection boxes were completely different from what I labeled

My version is the latest version, which uses the training set path for the validation set

Hello author, while using OBB to train my dataset, I found that the tilted boxes in the validation set images and training set images after training did not match. There were cases where the angles were skewed, and during my inference, the detection boxes were completely different from what I labeled

![image](https://github.com/user-attachments/assets/8bd861f0-3d27-4fac-acad-9a8bf499d032) ![image](https://github.com/user-attachments/assets/d22a857a-efdb-4e0b-8a74-ff27ecb99d56)