Fish Action Dataset Preparation

This folder contains scripts to prepare your fish action video dataset for SlowFast training.

Important: No Video Pre-splitting Required

SlowFast does NOT require you to pre-split videos into clips. The framework automatically:

Loads your full-length videos
Samples short clips (typically 2-4 seconds) during training
Randomly samples one clip per video for training/validation
Uniformly samples multiple clips per video for testing

The clip duration is determined by configuration parameters:

NUM_FRAMES: Number of frames to sample (e.g., 8, 16, 32, 64)
SAMPLING_RATE: Interval between sampled frames (e.g., 2, 8)
TARGET_FPS: Target frames per second (typically 30)

Example: With NUM_FRAMES=32 and SAMPLING_RATE=2, the clip duration is approximately: (32-1) × 2 / 30 ≈ 2.07 seconds

Dataset Structure

Your videos should be organized as follows:

~/data/fish/fish_action_videos/
├── class1/
│   ├── video1.mp4
│   ├── video2.mp4
│   └── ...
├── class2/
│   ├── video1.mp4
│   ├── video2.mp4
│   └── ...
└── ...

Each subfolder represents a class, and contains multiple video files.

Usage

Run the preparation script:

python dataset/prepare_fish_dataset.py \
    --video_dir ~/data/fish/fish_action_videos \
    --output_dir ./dataset

Options

--video_dir: Root directory containing class subfolders (default: ~/data/fish/fish_action_videos)
--output_dir: Output directory for CSV files (default: ./dataset)
--train_ratio: Ratio of videos for training (default: 0.7)
--val_ratio: Ratio of videos for validation (default: 0.15)
--test_ratio: Ratio of videos for testing (default: 0.15)
--seed: Random seed for reproducibility (default: 42)

Output

The script will generate:

train.csv: Training set with format path_to_video label
val.csv: Validation set with format path_to_video label
test.csv: Test set with format path_to_video label
label_map.txt: Mapping of label indices to class names

Configuration

After running the script, configure SlowFast by setting:

DATA.PATH_TO_DATA_DIR: Path to the output directory (where CSV files are located)
DATA.PATH_PREFIX: Path to the video directory (~/data/fish/fish_action_videos)

Example configuration:

DATA:
  PATH_TO_DATA_DIR: "/home/ubuntu/projects/FishAction/dataset"
  PATH_PREFIX: "/home/ubuntu/data/fish/fish_action_videos"

Supported Video Formats

The script supports common video formats:

.mp4, .avi, .mov, .mkv, .webm, .flv, .m4v

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