YORU v2.0.0 Beta 2

YORU v2.0.0 Beta 2

⚠️ This is a pre-release (beta) version. It may contain bugs. Please report any issues on GitHub Issues.

Highlights

• The launcher no longer needs Google Chrome. It now opens in a native window (pywebview) instead of a browser served over localhost:8889 — but this means you must update your environment before YORU will start.
• The bundled YOLOv5 code is gone. Detection and training now run through a plugin system built on the ultralytics package, torchvision, and a new ONNX backend. YOLOv5 is no longer trainable and old YOLOv5 .pt files can no longer be loaded.
• The Training GUI now tells you if your GPU has enough memory before you start, and lets you stop a run cleanly with "Stop after this epoch" instead of killing it. Plus a long list of real bug fixes — the closed-loop trigger was ignoring your confidence threshold and your pin number, screen-capture mode fed broken frames to the detector, and the Video Analysis window froze while it worked.

⚠️ Breaking Changes

1. You must update your environment before YORU will start

The launcher moved from Eel to pywebview, and onnxruntime was added. An environment made for Beta 1 has neither, so python -m yoru stops with [yoru] failed to import yoru.app.main: No module named 'webview'.

conda env update -f YORU.yml --prune

The launch command itself is unchanged: python -m yoru (or the yoru command). Google Chrome is no longer required, and nothing opens a network port any more.

2. YOLOv5 models can no longer be trained, and old YOLOv5 weights no longer load

The vendored yoru/libs/yolov5/ tree was removed. What to do with an existing YOLOv5 model:

• To keep using it for detection: export it to ONNX with the upstream YOLOv5 repository, then point yolo_model_path in your condition file at the .onnx file. The new ONNX backend understands the YOLOv5 output layout and is selected automatically from the file extension.
• Otherwise: retrain with YOLOv8 or YOLO11 in the Training GUI. Opening a v1.x / Beta 1 project does not error — the GUI swaps yolov5s.pt for yolo11s.pt (same size letter) and prints a notice — but the run starts from scratch and results are not comparable to your YOLOv5 baseline.
• Condition files that still say yolo_model_type: yolov5 keep loading (the name is aliased to ultralytics); it is the old weight file that cannot be read.

3. Check trigger_pin in your condition files

Beta 1 hard-coded the TTL output to digital pin 13 and silently ignored the trigger_pin value in your file. Beta 2 actually uses it. If any of your condition files sets trigger_pin to something other than 13, that pin is what will now fire — edit the YAML or rewire before your next experiment. Files with no trigger_pin key still default to 13.

4. Your closed-loop trigger will fire less often

trigger_threshold_configuration was loaded but never read in Beta 1, so the trigger fired on any detection of the trigger class regardless of confidence. It is now applied. With the shipped values (0.3–0.5) you should expect fewer firings from an unchanged config. If you had raised the threshold to compensate for it being ignored, lower it back to the value you actually want.

5. Trained models are now saved in exp_<model>/, not train/

Look for your weights in <project>/exp_yolo11s/weights/best.pt (YOLO / RT-DETR) or <project>/exp_fasterrcnn/fasterrcnn_best.pt (torchvision). Repeat runs go to exp_yolo11s2, exp_yolo11s3, … so retraining no longer overwrites earlier weights or writes checkpoints into your training-image folder. Existing train/ folders are untouched; update any scripts or notes that point at them.

6. If you wrote your own trigger plugin

Three things changed in the contract — check all three:

• The constructor must accept m_dict: def __init__(self, m_dict=None):, not def __init__(self):.
• The 3rd argument of trigger() is a pyfirmata board, not a serial port: use arduino.writeDO_all(1) / arduino.writeDO_all(0), not ser.write(b"1").
• Handle a missing board: if arduino is None: return (for configs with Arduino_COM: "None").
• Fix the import: import libs.arduino as ard → import yoru.libs.arduino as ard.

7. If you import YORU from your own scripts

yoru.libs.yolo_wrapper was deleted. Replace from yoru.libs.yolo_wrapper import load_yolo_model with:

from yoru.libs.plugins import get_detector
det = get_detector("auto", model_path)   # or "ultralytics", "rtdetr", "torchvision", "onnx"

The detector exposes .names and .detect(image), which takes a BGR image and returns a list of dicts with keys x1, y1, x2, y2, conf, class_id, class_name. yoru.libs.file_operation_evaluation was also removed (it was a duplicate of yoru.libs.file_operation_create_label).

8. Smaller changes to be aware of

• Detection thresholds are now the same for every backend (confidence 0.25, IoU 0.45). Previously each backend used its own default — torchvision models in particular will report a different number of detections than in Beta 1.
• ONNX inference now letterboxes instead of stretching the frame, so box coordinates on non-square inputs are geometrically correct but numerically different from Beta 1.
• The train/val split is now deterministic (seeded) and matches .jpg/.jpeg/.bmp/.tif/.tiff as well as .png. Do not re-split a project mid-experiment — you will get a different split than Beta 1 produced.
• Saved window layouts reset once: the custom_layout_*.ini files moved from config/ to logs/. You can delete the stale config/custom_layout_*.ini files.

What's New

Model Support

• New ONNX detection backend — point yolo_model_path at a .onnx file (or set yolo_model_type: onnx) and it is used automatically. Handles models exported from YOLOv5 and YOLOv8 / YOLO11, reads class names from the model metadata, and uses whatever ONNX Runtime execution providers are installed.
• Detection and training now go through a plugin registry (yoru/libs/plugins/) with ultralytics (YOLOv8 / YOLO11), rtdetr (RT-DETR), torchvision (Faster R-CNN / Mask R-CNN / SSD) and onnx backends; auto picks one from your weights file.
• Backend auto-detection no longer unpickles your checkpoint to identify it — it reads the file name and, if needed, the class-name table out of the archive.
• If a backend's dependency is missing, the error now names the backends that are available and why the others failed, instead of saying "unknown backend".
• Existing torchvision checkpoints from Beta 1 load unchanged.

Training GUI

• GPU memory estimate before you train. Step 6 shows a live line such as ~9.4 GB needed / 7.6 GB free (NVIDIA RTX 4070) with a breakdown, colour-coded green / orange / red, recalculated as you change model, Image Size and Batch. It counts free VRAM, so another training run or a live detection session on the same card is taken into account. Pressing Train Model while it is red offers "Use Batch n" (the largest batch expected to fit), "Train anyway" or "Cancel". Accurate to roughly ±30%.
• "Stop after this epoch". Ends the run gracefully: the current epoch finishes and is saved, the final validation pass runs, and both best.pt and last.pt stay usable. A red Force stop appears while a stop is pending, with a confirmation that spells out what is lost — and it kills the dataloader workers too, so nothing is left holding the GPU. A run started from a terminal can be stopped the same way by creating an empty .yoru_stop_request file in the project directory.
• Step 6 now reports how the run ended — Complete!!, or Stopped at epoch N / M with a message saying where the weights are — and Train Model is disabled during a run, so you can no longer start a second training subprocess by accident.
• The training console shows one line per epoch instead of ~161 (ultralytics' progress-bar redraws were arriving as separate lines).
• Training subprocesses now use the Python interpreter YORU is running under, so training works when YORU is started from another directory or when python is not the environment's interpreter.

Launcher and Windows

• The launcher is a native window — no browser, no local web server, no port 8889 — and it now renders correctly offline.
• Picking a config file that no longer exists shows an error dialog instead of only printing to the console; the selected condition file is shown in the window at startup.
• yoru gui remembers the last-used config instead of resetting to config/template.yaml, and yoru gui --config <file> now actually works (it was silently discarded in Beta 1).
• The real-time process can be started directly: python -m yoru.realtime_yoru_GUI path/to/condition.yaml.
• All windows are a uniform 1000×800. Note the Real-time Process window is narrower than in Beta 1 (was 1280×700).
• Launching Frame Capture or labelImg no longer freezes the window that launched it.

Configuration

• New optional key hardware.camera_settings_dialog (default False) — the camera driver's property dialog is now opt-in.
• All shipped condition files under config/ were rewritten: developer-machine paths and the dead root: key removed, export defaults to ./results/, a model path pointing at a file that does not exist fixed, Arduino_COM: 13 (a pin number in the COM field) fixed, curly quotes around “COM3” fixed, a trigger_style naming a plugin that does not exist fixed, and every key given an inline comment.
• Your existing condition files still load — the new keys are optional and unknown keys are ignored.

Documentation and Licensing

• docs/install.md gains an install with uv path: cd Path/to/YORU, uv sync, uv run python -m yoru.
• docs/training.md documents the GPU-memory estimate and the stop buttons; docs/evaluation.md points at the new exp_<model>/ folders.
• New THIRD_PARTY_LICENSES.md lists every dependency and its licence, and the README adds an Ultralytics dual-licensing notice: Ultralytics YOLO is AGPL-3.0 by default and that extends to models trained with it, so commercial use needs an Ultralytics Enterprise licence.
• New working-example.md records one verified working machine (Windows 11, RTX 5070 Ti, torch 2.8.0+cu128) as a reference when an install misbehaves.
• The README now has a Versions table making clear that v1.1.1 is the stable release and this is a preview.

Bug Fixes

• The closed-loop trigger ignored your confidence threshold. trigger_threshold_configuration was never read, so any detection of the trigger class fired the TTL. Anyone who ran closed-loop experiments on Beta 1 was effectively running with threshold 0.
• The TTL always came out of pin 13, whatever trigger_pin said in your condition file.
• Three of the five bundled trigger plugins could not run at all — standard_nidaq, state_convert and state_convert_for_copulation_attempts could not even be constructed, so the trigger simply never engaged; the two state_convert plugins also still wrote to a serial port that had become a pyfirmata board. The Arduino plugins no longer crash the trigger process when no board is connected.
• Turning the trigger off crashed the trigger process and left the COM port held open, so re-enabling it in the same session did nothing.
• Screen-capture mode (stream_MSS: True) fed 4-channel BGRA frames to the detector, the recorder and the display — recording and detection should now work where they previously produced broken output.
• Recorded video was written at the measured frame rate rather than the configured one, so clips played back at the wrong speed. A camera that cannot be opened, or returns no frame, now says so and names hardware.camera_id instead of failing cryptically.
• The Video Analysis window froze for the whole job ("Not Responding") and the progress bar never moved. Analysis now runs on a worker thread with live movie/image progress, remaining time and movies-left counters, buttons disabled while busy, and errors shown in the status line.
• Every rendered analysis video came out upside down — create_video() flipped unconditionally, ignoring the flip checkboxes — and it crashed on the last frame.
• A single below-threshold detection discarded the rest of that frame's detections in offline analysis.
• The train/val split silently skipped non-PNG datasets, producing split folders with labels but no images, and aborted on a label file ending in a blank line.
• Training runs overwrote each other and wrote checkpoints into the dataset folder (see breaking change 5).
• The real-time detection process spun a CPU core at full speed when detection was switched off, and died silently on a bad frame; it now sleeps between checks, and errors are logged and retried.
• Frame Capture and Refine stole your keyboard. Left / Right / Alt were registered as a global OS hook, so typing in another application stepped frames and saved images into your dataset. They are now normal window shortcuts that only fire when the YORU window has focus. Each grab also leaked a file handle, and grabbing before choosing an output folder crashed.
• Assorted crashes and leaks: the camera driver property dialog popping up on every real-time start; a full-width character making ser_recount unconstructible; nidaq.dio.stop() not actually stopping the DAQ task; a divide-by-zero in the evaluation IoU; a SciPy function removed in modern versions; Quit raising in the Evaluation and Create-Labels windows; and yoru gui opening a second launcher window when an error escaped the GUI.
• yoru --version now reports the real version instead of a placeholder, and the packaged source distribution now actually contains config/, trigger_plugins/ and web/.

Installation / Upgrading

Upgrading from Beta 1 (conda): this step is required — the launcher will not start otherwise.

conda activate yoru
conda env update -f YORU.yml --prune
python -m yoru

Fresh install (conda): follow docs/install.md. Google Chrome is no longer a prerequisite.

git clone https://github.com/Kamikouchi-lab/YORU.git
cd YORU
git checkout v2.0.0-beta.2
conda env create -f YORU.yml
conda activate yoru
python -m yoru

Alternative: install with uv (resolves everything from pyproject.toml / uv.lock, so the conda env-creation and manual PyTorch steps are not needed):

cd Path/to/YORU
uv sync
uv run python -m yoru

GPU note: the PyTorch install line in the README covers CUDA 11.8 and 12.1. RTX 50-series (Blackwell) cards need a newer build — see working-example.md, which records a working RTX 5070 Ti setup on torch 2.8.0+cu128.

After upgrading, check: the trigger_pin value in every condition file (breaking change 3), your trigger_threshold_configuration values (breaking change 4), and any script that points at <project>/train/weights/best.pt (breaking change 5).

Notes

• This release targets the develop4 branch and is not the stable release. For general lab use, v1.1.1 remains the recommended version.
• The GUI, camera, training and Arduino paths in this release were developed in an environment without a GPU, camera, display or Arduino — they need real-hardware testing. Reports from actual rigs are especially valuable right now.
• Feedback and bug reports are welcome!