PPC

A model that performs binary classification to determine whether the subject is holding a smartphone. 48x48 RGB image.

class_id	label	Model output index
0	no_possession	0
1	possession	1

The PyTorch model and exported ONNX model always return two probabilities in the following order: [no_possession_probability, possession_probability].

output_.mp4

Variant	Size	F1	CPU inference latency	ONNX
P	115 KB	0.8959	0.24 ms	Download
N	176 KB	0.9386	0.39 ms	Download
T	280 KB	0.9520	0.51 ms	Download
S	495 KB	0.9664	0.66 ms	Download
C	876 KB	0.9868	0.73 ms	Download
M	1.7 MB	0.9924	0.86 ms	Download
L	6.4 MB	0.9961	1.07 ms	Download

Data sample

no possession	no possession	possession	possession	possession	possession

Setup

The Python version and dependencies are pinned in pyproject.toml.

git clone https://github.com/PINTO0309/PPC.git && cd PPC
curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync
source .venv/bin/activate

Run the commands below in the managed environment by prefixing them with uv run.

Dataset

Place images under data/ using the following structure. Image filenames are not used to determine labels; only the top-level label directory is used.

data/
├── no_possession/
│   └── .../*.png
└── possession/
    └── .../*.png

Generate the Parquet dataset. By default, raw image bytes are embedded, and each class is split into train and validation sets using a seeded 90/10 split.

uv run python 02_make_parquet.py

To store image paths without embedding image bytes, run:

uv run python 02_make_parquet.py --no-embed-images --overwrite

To merge multiple PPC Parquet files, specify each input path relative to data/.

uv run python 03_merge_parquet.py dataset_a.parquet dataset_b.parquet --overwrite

An optional preprocessing command can generate crops and annotations from videos or labeled image directories. Use --detector-model to specify the detector ONNX model.

uv run python 01_data_prep_realdata.py \
--input-image-dir /path/to/labeled-images \
--detector-model /path/to/detector.onnx

Split counts:
  train: 39376
    val: 4375
Label counts:
     no_possession: 25981
        possession: 17770
Split/label counts:
  train    no_possession: 23383
  train       possession: 15993
    val    no_possession: 2598
    val       possession: 1777

Inference

uv run python demo_phone_gaze_classification.py \
-v 0 \
-pm ppc_l_48x48.onnx \
-dlr -dnm -dgm -dhm \
-ep cuda \
-gm gazelle_dinov3_vit_tiny_inout_1x3x640x640_1xNx4.onnx \
--enable-heatmap

uv run python demo_phone_gaze_classification.py \
-v 0 \
-pm ppc_l_48x48.onnx \
-dlr -dnm -dgm -dhm \
-ep tensorrt \
-gm gazelle_dinov3_vit_tiny_inout_1x3x640x640_1xNx4.onnx \
--enable-heatmap

Training Pipeline

• Use the labeled image folders under data/no_action, data/point_somewhere, and data/point.

• 02_make_parquet.py writes pre-defined train/val splits into data/dataset.parquet using an image-level 9:1 split per class.

• The training loop relies on BCEWithLogitsLoss plus class-balanced pos_weight to stabilise optimisation under class imbalance; inference produces sigmoid probabilities. Use --train_resampling weighted to switch on the previous WeightedRandomSampler behaviour, or --train_resampling balanced to physically duplicate minority classes before shuffling.

• Training history, validation metrics, optional test predictions, checkpoints, configuration JSON, and ONNX exports are produced automatically.

• Per-epoch checkpoints named like ppc_epoch_0001.pt are retained (latest 10), as well as the best checkpoints named ppc_best_epoch0004_f1_0.9321.pt (also latest 10).

• The backbone can be switched with --arch_variant. Supported combinations with --head_variant are:

  --arch_variant	Default (--head_variant auto)	Explicitly selectable heads	Remarks
  baseline	avg	avg, avgmax_mlp	When using transformer/mlp_mixer, you need to adjust the height and width of the feature map so that they are divisible by --token_mixer_grid (if left as is, an exception will occur during ONNX conversion or inference).
  inverted_se	avgmax_mlp	avg, avgmax_mlp	When using transformer/mlp_mixer, it is necessary to adjust --token_mixer_grid as above.
  convnext	transformer	avg, avgmax_mlp, transformer, mlp_mixer	For token mixer heads, the feature map dimensions must be divisible by --token_mixer_grid (default 2x3).

• The classification head is selected with --head_variant (avg, avgmax_mlp, transformer, mlp_mixer, or auto which derives a sensible default from the backbone).

• Pass --rgb_to_yuv_to_y to convert RGB crops to YUV, keep only the Y (luma) channel inside the network, and train a single-channel stem without modifying the dataloader.

• Alternatively, use --rgb_to_lab or --rgb_to_luv to convert inputs to CIE Lab/Luv (3-channel) before the stem; these options are mutually exclusive with each other and with --rgb_to_yuv_to_y.

• Mixed precision can be enabled with --use_amp when CUDA is available.

• Resume training with --resume path/to/ppc_epoch_XXXX.pt; all optimiser/scheduler/AMP states and history are restored.

• Loss/accuracy/F1 metrics are logged to TensorBoard under output_dir, and tqdm progress bars expose per-epoch progress for train/val/test loops.

Baseline depthwise-separable CNN:

SIZE=48x48
uv run python -m ppc train \
--data_root data/dataset.parquet \
--output_dir runs/ppc_${SIZE} \
--epochs 100 \
--batch_size 256 \
--train_resampling balanced \
--image_size ${SIZE} \
--base_channels 32 \
--num_blocks 4 \
--arch_variant baseline \
--seed 42 \
--device auto \
--use_amp

Inverted residual + SE variant (recommended for higher capacity):

SIZE=48x48
VAR=s
uv run python -m ppc train \
--data_root data/dataset.parquet \
--output_dir runs/ppc_is_${VAR}_${SIZE} \
--epochs 100 \
--batch_size 256 \
--train_resampling balanced \
--image_size ${SIZE} \
--base_channels 32 \
--num_blocks 4 \
--arch_variant inverted_se \
--head_variant avgmax_mlp \
--seed 42 \
--device auto \
--use_amp

ConvNeXt-style backbone with transformer head over pooled tokens:

SIZE=48x48
uv run python -m ppc train \
--data_root data/dataset.parquet \
--output_dir runs/ppc_convnext_${SIZE} \
--epochs 100 \
--batch_size 256 \
--train_resampling balanced \
--image_size ${SIZE} \
--base_channels 32 \
--num_blocks 4 \
--arch_variant convnext \
--head_variant transformer \
--token_mixer_grid 2x2 \
--seed 42 \
--device auto \
--use_amp

• Outputs include the latest 10 ppc_epoch_*.pt, the latest 10 ppc_best_epochXXXX_f1_YYYY.pt (highest validation F1, or training F1 when no validation split), history.json, summary.json, optional test_predictions.csv, and train.log.
• After every epoch a confusion matrix and ROC curve are saved under runs/ppc/diagnostics/<split>/confusion_<split>_epochXXXX.png and roc_<split>_epochXXXX.png.
• --image_size accepts either a single integer for square crops (e.g. --image_size 48) or HEIGHTxWIDTH to resize non-square frames (e.g. --image_size 64x48).
• Add --resume <checkpoint> to continue from an earlier epoch. Remember that --epochs indicates the desired total epoch count (e.g. resuming --epochs 40 after training to epoch 30 will run 10 additional epochs).
• Launch TensorBoard with:

  tensorboard --logdir runs/ppc

ONNX Export

uv run python -m ppc exportonnx \
--checkpoint runs/ppc_is_s_48x48/ppc_best_epoch0049_f1_0.9939.pt \
--output ppc_s_48x48.onnx \
--opset 17

• The saved graph exposes images as input and prob_pointing as output (batch dimension is dynamic); probabilities can be consumed directly.
• After exporting, the tool runs onnxsim for simplification and rewrites any remaining BatchNormalization nodes into affine Mul/Add primitives. If simplification fails, a warning is emitted and the unsimplified model is preserved.

Arch

Ultra-lightweight classification model series

1. VSDLM: Visual-only speech detection driven by lip movements - MIT License
2. OCEC: Open closed eyes classification. Ultra-fast wink and blink estimation model - MIT License
3. PGC: Ultrafast pointing gesture classification - MIT License
4. SC: Ultrafast sitting classification - MIT License
5. PUC: Phone Usage Classifier is a three-class image classification pipeline for understanding how people interact with smartphones - MIT License
6. HSC: Happy smile classifier - MIT License
7. WHC: Waving Hand Classification - MIT License
8. UHD: Ultra-lightweight human detection - MIT License
9. MWC: Mask wearing classifier - MIT License
10. SGC: Classification of wearing vs. not wearing sunglasses. 48x48. - MIT License
11. HHC: Head Hat Classification. HHC is a binary classifier for cropped head images. 48x48. - MIT License
12. BPC: Background Plain classification. 48x48. - MIT License
13. PPC: Binary classification to determine whether the subject is holding a smartphone. 48x48 RGB image. - MIT License

Citation

If you find this project useful, please consider citing:

@software{hyodo2025ppc,
  author    = {Katsuya Hyodo},
  title     = {PINTO0309/PPC},
  month     = {07},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.21422276},
  url       = {https://github.com/PINTO0309/ppc},
  abstract  = {Binary classification to determine whether the subject is holding a smartphone. 48x48 RGB image.},
}