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  1. .gitignore +2 -0
  2. README.md +179 -0
  3. SHA256SUMS +86 -0
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+ /.prepare-state.json
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+ *.partial
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+ ---
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+ task_categories:
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+ - object-detection
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+ tags:
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+ - event-camera
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+ - v2e
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+ - voxel-grid
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+ - yolo
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+ viewer: false
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+ ---
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+
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+ # coco-v2e-native-voxel5
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+
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+ Five-channel event voxel grids with YOLO object-detection annotations, prepared
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+ from an evbench benchmark directory. TIFF images and label files are preserved
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+ byte-for-byte. Raw event streams and original RGB images are not included.
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+
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+ ## Contents and splits
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+
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+ | Physical split | Samples | Boxes |
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+ | --- | ---: | ---: |
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+ | train | 586,283 | 4,127,651 |
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+ | val | 24,760 | 176,381 |
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+
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+ The YAML split mappings are:
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+
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+ - `train` uses `images/train`.
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+ - `val` uses `images/val`.
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+
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+ When validation and test point to the same directory, they contain the same samples;
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+ there is no independent validation set. Select a separate validation subset from
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+ training data, grouped by original source image/sequence, before tuning a model.
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+
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+ Each uncompressed TAR shard contains paired members:
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+
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+ ```text
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+ images/<split>/<sample-key>.tif
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+ labels/<split>/<sample-key>.txt
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+ ```
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+
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+ Shards are sorted by sample key and keep each TIFF/label pair together. These are
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+ download-and-extract archives, rather than WebDataset-format samples. The Hub
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+ dataset viewer is disabled; load the extracted files with the training pipeline.
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+
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+ ## Representation and annotations
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+
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+ Each TIFF has five grayscale pages, one per temporal bin, in time order. Preserve
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+ all five pages when decoding. The renderer produces uint8 signed-polarity (ON − OFF)
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+ voxel counts over a 50.0 ms window, normalized by the 99th percentile of
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+ nonzero absolute counts across the grid, clipped to [-1, 1], and encoded as
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+ `uint8(128 + 127 * value)`. A value of 128 denotes no net events.
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+
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+ Labels are UTF-8 text: one `class_id center_x center_y width height` row per object.
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+ Coordinates are normalized to image dimensions; class IDs are zero-based.
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+ Empty label files denote samples with no boxes.
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+
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+ | ID | Class |
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+ | ---: | --- |
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+ | 0 | person |
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+ | 1 | bicycle |
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+ | 2 | car |
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+ | 3 | motorcycle |
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+ | 4 | airplane |
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+ | 5 | bus |
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+ | 6 | train |
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+ | 7 | truck |
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+ | 8 | boat |
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+ | 9 | traffic light |
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+ | 10 | fire hydrant |
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+ | 11 | stop sign |
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+ | 12 | parking meter |
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+ | 13 | bench |
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+ | 14 | bird |
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+ | 15 | cat |
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+ | 16 | dog |
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+ | 17 | horse |
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+ | 18 | sheep |
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+ | 19 | cow |
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+ | 20 | elephant |
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+ | 21 | bear |
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+ | 22 | zebra |
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+ | 23 | giraffe |
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+ | 24 | backpack |
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+ | 25 | umbrella |
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+ | 26 | handbag |
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+ | 27 | tie |
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+ | 28 | suitcase |
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+ | 29 | frisbee |
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+ | 30 | skis |
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+ | 31 | snowboard |
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+ | 32 | sports ball |
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+ | 33 | kite |
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+ | 34 | baseball bat |
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+ | 35 | baseball glove |
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+ | 36 | skateboard |
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+ | 37 | surfboard |
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+ | 38 | tennis racket |
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+ | 39 | bottle |
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+ | 40 | wine glass |
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+ | 41 | cup |
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+ | 42 | fork |
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+ | 43 | knife |
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+ | 44 | spoon |
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+ | 45 | bowl |
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+ | 46 | banana |
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+ | 47 | apple |
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+ | 48 | sandwich |
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+ | 49 | orange |
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+ | 50 | broccoli |
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+ | 51 | carrot |
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+ | 52 | hot dog |
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+ | 53 | pizza |
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+ | 54 | donut |
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+ | 55 | cake |
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+ | 56 | chair |
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+ | 57 | couch |
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+ | 58 | potted plant |
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+ | 59 | bed |
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+ | 60 | dining table |
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+ | 61 | toilet |
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+ | 62 | tv |
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+ | 63 | laptop |
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+ | 64 | mouse |
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+ | 65 | remote |
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+ | 66 | keyboard |
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+ | 67 | cell phone |
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+ | 68 | microwave |
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+ | 69 | oven |
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+ | 70 | toaster |
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+ | 71 | sink |
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+ | 72 | refrigerator |
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+ | 73 | book |
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+ | 74 | clock |
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+ | 75 | vase |
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+ | 76 | scissors |
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+ | 77 | teddy bear |
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+ | 78 | hair drier |
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+ | 79 | toothbrush |
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+
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+ ## Provenance
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+
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+ `meta.json` preserves the frame build settings and original statistics.
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+ The sequence metadata records source `coco`, modality `v2e`, preset `calibrated`, resolution 640 × 480, sequence duration 500 ms and label interval 100000 µs.
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+
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+ When available, `sequence_meta.json` preserves the upstream sequence export settings.
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+ Machine-specific absolute paths are replaced with `<local-path>/<basename>`.
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+ `manifest.json` records the actual packaged sample/box counts and shard hashes.
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+ The build-time `v2e_preset` argument alone does not establish how upstream sequences
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+ were simulated; consult the sequence metadata and generating code.
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+
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+ TODO before public release: add the generating code URL/commit, source-dataset
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+ citations and a citation for this derived dataset.
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+
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+ ## Download, verify and extract
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+
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+ ```bash
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+ pip install --upgrade huggingface_hub pyyaml
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+ hf download evbench/coco-v2e-native-voxel5 --repo-type dataset --local-dir ./dataset
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+ cd dataset
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+ python extract.py
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+ ```
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+
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+ The extracted root contains `data.yaml`, `images/` and `labels/`.
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+ Pass the **absolute path** of `data.yaml` to evbench or Ultralytics. The release YAML
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+ omits the machine-specific `path`. `extract.py` verifies all release checksums,
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+ extracts the shards and sets `path` to your local root for compatibility with evbench.
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+ This changes the local YAML, so checksum verification is intended for a fresh download,
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+ before extraction. The extraction helper requires Python >=3.11.8 and PyYAML.
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+ The configuration retains `channels: 5` and the original split mappings.
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+
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+ In an evbench environment, for example:
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+
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+ ```bash
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+ python -m evbench.train --data /absolute/path/to/dataset/data.yaml --model yolo11s.pt
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+ ```
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+
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+ ## License
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+
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+ TODO before public release: document the applicable source-dataset terms and the license for this derived release. No license has been inferred.
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