gazekit personal gaze models

Gaze-estimation models trained with gazekit, a webcam eye-tracking toolkit with a full personal training lifecycle (calibration, VOR/posture collection scenarios, ambient background training, clean/train/validate/evaluate/update iteration).

These weights are personalized: they were trained on one user's face, one camera, and one screen. They will not work well for anyone else — treat them as a reference artifact / starting checkpoint, and run the gazekit pipeline to train your own.

Files

file	description
gaze_cnn.pt	MobileNetV2 backbone (ImageNet init, first conv adapted to grayscale), two 64x48 eye crops + head pose (yaw/pitch/roll) → normalized screen (x, y). See gazekit/cnn.py for the exact architecture.
gaze_model.pkl	Linear ridge on the 19-dim "v5-combo" features (binocular iris + distance-gain + pose interactions). Requires gazekit at commit f3a6261 or later — the feature transform lives in gazekit/model.py, the pickle only stores the fitted pipeline.

Usage

from gazekit.cnn import CnnPredictor
pred = CnnPredictor("gaze_cnn.pt", screen_size=(1920, 1080))
# obs comes from gazekit.tracker.FaceTracker(...).process(frame, want_crops=True)
xy = pred.predict(obs)

Training data

Personal dataset (private): dwell-point calibration grids, VOR head-movement sweeps, multi-posture grids, screen-edge points, smooth-pursuit sweeps, and ambient popup samples, cleaned by the gazekit iterate pipeline.