docs: Improve predict_tile documentation with examples and tips

[Prathamesh8989]

Summary

This PR enhances the predict_tile user guide by adding detailed tips and parameter explanations to improve clarity for new users.

Improvements Included

• Added Key Parameters
  Included a clear breakdown of:

  • patch_size
  • patch_overlap
  • dataloader_strategy

• New Example Usage
  Added a clean, standalone code block demonstrating tiled prediction on large rasters.

• Grammar & Flow
  Refined the documentation text for improved readability and a more professional tone.

• Code Consistency

  • Standardized variable naming to consistently use model
  • Added missing pandas and os imports in the predict_file section

---

Related Issue(s)

This change improves guidance for handling large geospatial raster images, particularly around memory management and patch sizing during prediction.

---

AI-Assisted Development

• I used AI tools (e.g., GitHub Copilot, ChatGPT, etc.) in developing this PR
• I understand all the code I'm submitting
• I have reviewed and validated all AI-generated code

AI Tools Used

• ChatGPT: Used for correcting grammatical issues and improving the flow of documentation text
• Gemini: Used for structuring the documentation merge and verifying Python code consistency

[henrykironde]

Thank you for the contribution @Prathamesh8989

[henrykironde]

Use model.load_model(model_name="weecology/deepforest-tree", revision="main")

[Prathamesh8989]

Hi @henrykironde ,

Thanks for the suggestion!
I’ve updated the documentation to use
model.load_model(model_name="weecology/deepforest-tree", revision="main")
instead of model.use_release() and pushed the changes.