MobileGaze-MobileOne-S0-MLX-fp32
MLX port of the MobileOne-S0 gaze estimator from yakhyo/gaze-estimation (MIT). Predicts gaze yaw and pitch in degrees from a 448×448 face crop. Weights are the upstream reparameterized checkpoint converted verbatim; no retraining, no quantization.
At a glance
| Source | yakhyo/gaze-estimation @ 27efe989fc45, release weights/mobileone_s0.pt |
| Source sha256 | f50c99087f85f9a4fc8d75bd394262b6fcbc4ae21112928f67fabbf840664c95 |
| Format | MLX, single model.safetensors (1,238,004 params, float32) + config.json |
| Training data | Gaze360 (upstream) |
| Upstream accuracy | 12.58° mean angular error on Gaze360 (upstream README) |
| Decode | 90 bins × 4° − 180° (expected bin index after softmax) |
Parity gate
PASS — MLX graph vs. the fused torch oracle on 4 seeded
image-like inputs on 2026-10-04 (arm64 Darwin 27.0.0, mlx 0.32.2).
Worst decoded-angle difference 1.07e-04° (tolerance
0.001°); worst relative logit difference
8.88e-07 (tolerance 0.0001).
| sample | torch yaw | mlx yaw | torch pitch | mlx pitch | max Δ° |
|---|---|---|---|---|---|
| 0 | 36.454 | 36.454 | 17.295 | 17.295 | 4.58e-05 |
| 1 | 35.854 | 35.854 | 22.446 | 22.446 | 1.07e-04 |
| 2 | 33.624 | 33.624 | 20.521 | 20.521 | 6.10e-05 |
| 3 | 38.254 | 38.254 | 20.445 | 20.445 | 6.10e-05 |
Evidence: .sisyphus/evidence/mobilegaze-mlx/fp32/parity.json in ajentik/majek.
Usage
import mlx.core as mx
from PIL import Image
from pipelines.mobilegaze_mlx import from_pretrained, preprocess
model = from_pretrained("majentik/MobileGaze-MobileOne-S0-MLX-fp32")
face = Image.open("face_crop.jpg").convert("RGB").resize((448, 448))
x = preprocess(mx.array(__import__("numpy").asarray(face)))
yaw_deg, pitch_deg = model.predict_degrees(x)
print(float(yaw_deg[0]), float(pitch_deg[0]))
Input: tight face crop (upstream uses the RetinaFace box without expansion), RGB,
resized so the shorter side is 448, ImageNet mean/std. The model expects NHWC in MLX.
config.json carries the bins, bin width, angle offset, input size and normalisation.
Compatibility notes
- Inference-mode graph only (
reparam_conv+ ReLU per block); the training-time multi-branch MobileOne blocks are not represented. - Gaze360 angles are camera-relative; apps that need screen coordinates must add their own head-pose/viewing-distance calibration.
- Wellness/research use. Not validated as a medical or safety device.
Reproduce
uv run python -m pipelines.mobilegaze_mlx_release --tiers fp32 --dry-run
uv run python -m pipelines.mobilegaze_mlx_release --tiers fp32
License
MIT, inherited from yakhyo/gaze-estimation. Converted weights are redistributed under the same
terms with attribution to the upstream author.