Comments (2)
Thank you for your interest!
- Correct. If you do not plan to retrain the model using RUGD and RELLIS, no need to run data preprocessing.
- Yes. If the angle and the height from the ground are different, you might need to retrain it with your own dataset. We have not explore much its domain adaptation capability, other than doing some navigation testing in our environment. We did notice that the performance is not as good in real-world compared to the results on the dataset, so you should be able to get better results if you build a custom dataset and finetune it.
- I don't have an instruction provided, but if you go through the code, it should be straight forward. You can try to put the data in an existing dataset format and copy the code. You may need to adjust the name of the dataset and classes in a few places.
Best,
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Thanks for the reply.
Yeah, I was able to create custom dataset.
I might get back to you as I progress!
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Related Issues (20)
- Check point file HOT 10
- about the training loss become nan HOT 6
- Pre-processing images HOT 2
- AssertionError HOT 8
- Enquiry regarding Performance of Training HOT 14
- Error in ONNX conversion HOT 10
- how to get segmentation cost map from segmentation results? HOT 1
- Conversion of RUGD and Rellis Datasets To Rugd6 Group & Training HOT 4
- Is the onnx file generated by pytorch2onnx.py quantized? HOT 1
- Welcome update to OpenMMLab 2.0 HOT 1
- Use pretrained ResNet50 model (backbone) on ImageNet.
- L1? L2? L3? HOT 2
- checkpoint error HOT 4
- Output image of the model HOT 5
- ModuleNotFoundError: No module named 'mmcv._ext' HOT 4
- Change of backbone HOT 2
- Training on GOOSE Dataset HOT 3
- rellis folder structure
- output quality HOT 2
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