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๐Ÿš€ SnapPix

Official Code for ๐Ÿ“„ DAC'25 Paper: SnapPix: Efficient-Codingโ€“Inspired In-Sensor Compression for Edge Vision ๐Ÿ“š Paper Link


๐Ÿ“‚ Dataset

๐Ÿ”— Source

We use OpenMMLab Datasets for all dataset used.๐Ÿ“ฆ

โš™๏ธ Data Preprocessing

Apply downsampling, inverse gamma correction, and grayscale conversion to dataset, here is an example for SSV2:

python preprocess_data.py ../../OpenDataLab___sthv2/raw/sthv2/sthv2/videos/ ssv2_processed/ --input_format .webm

See dataset/preprocessing.sh for more examples.

Process csv of SSV2:

# for finetuning dataset
python3 dataset/ssv2_list_process.py input.csv output.csv
# for pretraining dataset
python3 dataset/ssv2_list_process.py input.csv output.csv --pretrained

๐Ÿงน Generate K710 (Pretrain Dataset)

Combining preprocessed K400 / K600 / K700 / SSV2 into one dataset:

python3 combine_pretrained.py dataset_lists/K710/train.csv dataset_lists/SSV2/train.csv \
mmdataset/k400_processed mmdataset/k600_processed mmdataset/k700_processed mmdataset/ssv2_processed combined_pretrain

Copy K400 / K600 / K700 to K710:

bash dataset/copy_k710.sh

๐Ÿ› ๏ธ Environment Setup

Refer to the VideoMAEv2 README for detailed environment installation instructions โœ…


๐Ÿงช Decorrelated Pattern Training

Train using the decorrelation strategy:

python3 VideoMAEv2/run_decorrelation_training.py

๐Ÿ” A pretrained version is available at:
VideoMAEv2/decorrelation_training_wd0_norm_new


๐Ÿ‹๏ธ๏ธ Pretraining Scripts

Located in:
VideoMAEv2/scripts/pretrain_and_reconstruct

Examples:

๐Ÿ“Œ Key Parameters:

  • OUTPUT_DIR: Path to logs and checkpoints ๐Ÿ“
  • DATA_PATH: CSV list of data files ๐Ÿ“„
  • --data_root: Dataset root (e.g., /local_scratch/26477563/mmdataset/) ๐Ÿ—‚๏ธ

๐ŸŽฏ Finetuning Scripts

Found in scripts/finetune/

Examples:

๐Ÿ”ง Key Parameters:

  • OUTPUT_DIR: Log/checkpoint directory
  • DATA_PATH: Dataset list path
  • MODEL_PATH: Path to pretrained model
  • --data_root: Dataset directory

๐Ÿ“Š Evaluation Scripts

Evaluate on different datasets using:

  • ๐Ÿ“ผ scripts/K400_precise_val/ โ€” Kinetics-400
  • ๐ŸŽฎ scripts/SSV2_precise_val/ โ€” Something-Something V2
  • ๐Ÿ“น scripts/UCF_precise_val/ โ€” UCF-101

๐Ÿ™ Acknowledgements

A big thank you to:

  • ๐Ÿง  VideoMAEv2 authors (Wang et al., CVPR 2023)
    ๐Ÿ”— VideoMAEv2 GitHub

  • ๐ŸŽฅ Action Recognition from a Single Coded Image
    ๐Ÿ“„ IEEE Paper

We greatly appreciate the open-source / code-sharing contributions that made SnapPix possible ๐Ÿ’ก