Improve dataset handling with adaptive folder detection

[yyq19990828]

Summary

This PR improves the dataset handling functionality in rfdetr/datasets/coco.py by adding adaptive folder detection and better fallback mechanisms for test datasets.

Key improvements:

• Adaptive val/valid folder detection: Automatically detects whether the dataset uses val (YOLO format) or valid (COCO format) folder naming
• Smart test dataset fallback: When test dataset is missing (which is common in most datasets), automatically uses the validation dataset as fallback
• Reduced manual dataset modification: Users no longer need to manually rename folders to match expected naming conventions
• Better error handling: Proper FileNotFoundError when neither val nor valid folders exist
• Code internationalization: Converted Chinese comments to English

Why this change?

• Most datasets don't include a separate test split, making the fallback mechanism necessary
• YOLO datasets typically use val folder while COCO datasets use valid folder
• This reduces the friction for users when working with different dataset formats without requiring manual folder restructuring

Test plan

• Verify the code handles both val and valid folder structures
• Confirm test dataset fallback works when test folder is missing
• Ensure proper error handling when validation folders are missing

🤖 Generated with Claude Code

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[isaacrob-roboflow]

I think this is interesting but I will say that datasets SHOULD include a test set that's not the val set ;) often nowadays people benchmark on COCO val, but that's not because that form is fine, it's because the test set is hidden away on a private server .. ultralytics notably does NOT report test set numbers on datasets they train on, but imo that gives a misleading measure of final accuracy because they're also picking the best checkpoint based on val score so the result is biased

so I would still like it to be clear when folks train a model that they SHOULD have a test set .. but handling val vs valid seems logical to me

[Borda]

We have added YOLO dataset support in 1.4, so is this still needed?