point model training support

[jveitchmichaelis]

Description

This PR adds training functionality for the default point model. Most of the code has been distilled from my experimental branch. It should replicate the existing checkpoint, and I'm running a quick test to make sure.

Changes are relatively small in scope - added the optimal transport loss code, fleshed out compute_losses for the model and fixed a small bug in point visualization where the VertexAnnotator wouldn't accept a palette (we instead convert points to "detections" with a radius and plot them as circles).

I've included a point_pretrain config which was used to train the current default point model on NEON data, replication should be possible with:

uv deepforest --config-name point_pretrain train

Related Issue(s)

Closes #809 as we now support training, visualization of predictions, simple unit tests + should also include multi-class support in theory but we've not tested it.

AI-Assisted Development

Claude code for assistance with merging the changes from the other branch.

• 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

[codecov]

Codecov Report

❌ Patch coverage is 92.96875% with 18 lines in your changes missing coverage. Please review.
✅ Project coverage is 87.05%. Comparing base (d2851ec) to head (e538222).
⚠️ Report is 7 commits behind head on main.

Files with missing lines	Patch %	Lines
src/deepforest/losses/ot_loss.py	93.75%	7 Missing ⚠️
src/deepforest/models/treeformer.py	94.30%	7 Missing ⚠️
src/deepforest/main.py	42.85%	4 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1396      +/-   ##
==========================================
+ Coverage   86.61%   87.05%   +0.43%     
==========================================
  Files          26       27       +1     
  Lines        3736     3977     +241     
==========================================
+ Hits         3236     3462     +226     
- Misses        500      515      +15     

Flag	Coverage Δ
unittests	87.05% <92.96%> (+0.43%)	⬆️

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[jveitchmichaelis]

This looks good enough for review. I’ll run some more extensive tests to see if there’s any other features that need to be pulled in, but the core losses are all here.