Create README.md

README.md

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+*Important.* This dataset is part of the [**torchmil** library](http://torchmil.readthedocs.io/).
+
+
+Traffic Anomaly Detection (TAD) dataset adapted for Multiple Instance Learning (MIL).
+
+
+### About the Original Traffic Anomaly Detection (TAD) Dataset
+
+The original [Traffic Anomaly Detection (TAD) dataset](https://www.kaggle.com/datasets/nikanvasei/traffic-anomaly-dataset-tad) contains video clips. Each clip is labeled to indicate whether it contains an anomaly or not; however, frame-level labels are not available 
+
+### Dataset Description
+
+We have preprocessed the videos by computing features for each frame using various feature extractors.
+
+- A **video** is labeled as positive (`label=1`) if it contains evidence of traffic anomaly.
+- A **video** is labeled as positive (`label=1`) if it contains at least one positive frame.
+
+This means a video is considered positive if there is any evidence of traffic anomaly.
+
+### Directory structure
+
+The following directory structure is expected:
+
+```
+root
+├── features
+│   ├── features_{features}
+│   │   ├── video1.npy
+│   │   ├── video2.npy
+│   │   └── ...
+├── labels
+│   ├── video1.npy
+│   ├── video2.npy
+│   └── ...
+└── splits.csv
+```
+
+Each `.npy` file corresponds to a video. The `splits.csv` file defines train/test splits for standardized experimentation.