Add annotations, timing metadata, and final shard checksums
Browse files- README.md +63 -0
- annotations/README.md +44 -0
- annotations/activitynet_actions.json +0 -0
- annotations/activitynet_captions.json +0 -0
- annotations/annotation_issues.jsonl +266 -0
- annotations/eventactivitynet_alignment.json +0 -0
- docs/CHANGELOG.md +8 -0
- docs/DATASET_CARD.md +63 -0
- docs/DATASET_FORMAT.md +50 -0
- docs/DATASET_GENERATION.md +41 -0
- docs/LICENSE_NOTES.md +4 -0
- docs/RELEASE_NOTES.md +49 -0
- metadata/shard_checksums.sha256 +219 -0
- metadata/shard_summary.json +12 -2
- metadata/video_metadata.jsonl +0 -0
README.md
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@@ -150,3 +150,66 @@ Final technical validation confirmed:
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- required datasets and dtypes are present in every file;
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- Large split counts are 2,316 train and 947 validation;
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- total release size is approximately 4.36 TB.
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- required datasets and dtypes are present in every file;
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- Large split counts are 2,316 train and 947 validation;
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- total release size is approximately 4.36 TB.
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## Payload Files
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The HDF5 payload is distributed as deterministic uncompressed tar shards:
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- 157 train tar shards under `data/train/`;
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- 62 validation tar shards under `data/validation/`;
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- 219 tar shards total;
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- 3,263 HDF5 members total;
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- one HDF5 member per released ActivityNet video.
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Large, Medium, and Small share the same physical HDF5 payload. Medium and Small are selected using `scales/medium_ids.txt` and `scales/small_ids.txt`; they do not duplicate payload files.
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## Annotation Files
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Additional public annotation files are provided under `annotations/` and `metadata/`:
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- `annotations/activitynet_captions.json`: ActivityNet Captions timestamped natural-language descriptions for release videos. Validation references preserve `val_1` and `val_2` separately.
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- `annotations/activitynet_actions.json`: ActivityNet v1.3 temporal action segments and labels.
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- `annotations/eventactivitynet_alignment.json`: EventActivityNet project-derived caption/action alignment generated using temporal IoU with midpoint-distance fallback.
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- `annotations/annotation_issues.jsonl`: known upstream annotation quirks recorded without changing canonical values.
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- `metadata/video_metadata.jsonl`: original-rate timing metadata, including exact rational FPS where available.
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Captions are timestamped descriptions from ActivityNet Captions. They are not instruction-tuning examples.
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## Timing Metadata
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EventActivityNet v1.0 uses original-rate, variable-FPS ActivityNet videos. The released HDF5 files were not generated from `anet_240fps_v2` or `anet_240fps_old`.
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Implementation-derived timing:
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- one event slice is generated for each adjacent decoded source-frame transition `(e, e + 1)`;
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- one full `events[t]` tensor groups five adjacent-frame transitions and has shape `(5, H, W)`;
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- for source frame count `N`, `events_T = ceil((N - 1) / 5)`;
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- voxel `t` covers event-slice range `[5*t, 5*t + 5)`, clipped to available transitions `[0, N - 1)`;
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- the corresponding source-frame interval is `[5*t, min(5*t + 5, N - 1)]`;
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- with rational FPS `fps_num / fps_den`, the approximate seconds interval is `[5*t * fps_den / fps_num, min(5*t + 5, N - 1) * fps_den / fps_num]`;
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- the final voxel may be partial, with `voxel_event_count[t]` smaller than 5;
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- HDF5 root `fps` is the original source-frame FPS captured by OpenCV, not a 240 fps derivative;
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- `voxel_event_start[t]` is the generated event-slice start index for voxel `t`;
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- `voxel_event_count[t]` is the number of generated adjacent-frame slices in voxel `t`;
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- `voxel_event_start` and `voxel_event_count` are not timestamps and not pixel-event counts.
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For caption/action interval `[start_seconds, end_seconds]`, use original FPS to compute:
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```text
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start_frame = floor(start_seconds * fps_num / fps_den)
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end_frame = ceil(end_seconds * fps_num / fps_den)
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t_start = max(0, floor(start_frame / 5))
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t_end_exclusive = min(events_T, ceil(end_frame / 5))
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```
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Use `[t_start, t_end_exclusive)` for Python slicing, or `[t_start, t_end_exclusive - 1]` as an inclusive range when non-empty. Do not use `time_seconds = t / fps` for voxel starts; voxel start time is approximately `5 * t / fps`.
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## Checksums
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Tar shard checksums are published in `metadata/shard_checksums.sha256`. To verify downloaded shards from the repository root:
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```bash
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sha256sum -c metadata/shard_checksums.sha256
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```
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The checksum file contains one repository-relative entry for each of the 219 tar shards.
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annotations/README.md
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@@ -0,0 +1,44 @@
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# EventActivityNet Annotation and Original-Rate Timing Metadata
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This package contains public annotation and timing metadata for the EventActivityNet v1.0 Large release. It is filtered to the canonical 3,263 release video IDs. Medium and Small scales reuse these same files by filtering with their existing video-ID lists.
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## Files
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- `annotations/activitynet_captions.json`: ActivityNet Captions entries for release videos. Training videos have the `train` caption set; validation videos may include separate `val_1` and `val_2` caption sets.
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- `annotations/activitynet_actions.json`: ActivityNet v1.3 action annotations for release videos.
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- `annotations/eventactivitynet_alignment.json`: Project-generated action-caption alignment. The alignment was generated with temporal IoU between action segments and caption timestamps, with midpoint-distance fallback, using `align_v13_with_captions.py`.
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- `annotations/annotation_issues.jsonl`: Known source annotation quirks preserved without modification.
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- `metadata/video_metadata.jsonl`: Original-rate production timing metadata for each release video.
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## Timing
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EventActivityNet v1.0 uses original-rate, variable-FPS source videos. It was not generated from the interpolated 240 fps video derivatives. Caption and action timestamps remain in seconds on the original ActivityNet video timeline.
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For a video with `source_frame_count = N`, `num_bins = 5`, and `frames_per_bin = 1`, the production tensor length is:
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```text
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events_T = ceil((N - 1) / 5)
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```
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Voxel index `i` accumulates adjacent-frame event slices beginning at slice `5*i`. Its approximate source-frame interval is `[5*i, min(5*i + 5, N - 1)]`, and its approximate seconds interval is `[5*i / fps, min(5*i + 5, N - 1) / fps]`. Use the exact rational FPS fields where available.
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## Known Source Annotation Quirks
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Canonical source annotations are preserved as-is. Some source timestamps or action segments have minor ordering or boundary issues, including small floating-point overshoots beyond the recorded duration. Consumers should use a small numerical tolerance when validating temporal boundaries; the issue file records the exact observed cases and overshoot values.
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## Source Hashes
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Source SHA256 hashes used to build this package:
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```json
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{
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"activitynet_actions": "4c29d5b1561e1cbff9ac69816c159e60d417a18f16db8acc0fc254c377fa9ae3",
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"alignment": "40a75cfd6a053011766e80e5a6d449b84a3ececf51b1f34cfa33eefba8729d36",
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"captions_train": "704536b2779011c1a729c927ae30e39ea9c90c38557836bbe969499917243d69",
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"captions_val_1": "466aaccb9ebe45c6db4c98488a4ddbeb5b22faffe62e1bbce81222c14fc29e1c",
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"captions_val_2": "08fe56568bc92dd02a6150fe1094457a5f2ac72975b8778a922bc9a65aa7f0e9",
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"class_mapping": "f15c7864abe012bd82b1df2c33beae8254acffc4b86a3dcbf6174c02a61eb400",
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"large_manifest": "02468b7f301a85c5f3776b72b9b1ff83635bc99dd6b2ce42a7c7e5308007c96a",
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"verify_all": "a5d17bcb508789c6e96747d3eac9ecee13b99dd683df995cd81e3179966bd7bc"
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}
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```
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annotations/activitynet_actions.json
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The diff for this file is too large to render.
See raw diff
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annotations/activitynet_captions.json
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The diff for this file is too large to render.
See raw diff
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annotations/annotation_issues.jsonl
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| 1 |
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{"annotation_set": "val_1", "annotation_source": "ActivityNet Captions", "duration": 215.82999999999998, "index": 2, "issue_type": "caption_timestamp_bounds", "overshoot_seconds": 2.842170943040401e-14, "timestamp": [67.99, 215.83], "video_id": "v_-A6e83tl4Y8"}
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| 2 |
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{"annotation_set": "val_2", "annotation_source": "ActivityNet Captions", "duration": 215.82999999999998, "index": 0, "issue_type": "caption_timestamp_bounds", "overshoot_seconds": 2.842170943040401e-14, "timestamp": [0, 215.83], "video_id": "v_-A6e83tl4Y8"}
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| 3 |
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{"annotation_set": "val_2", "annotation_source": "ActivityNet Captions", "index": 2, "issue_type": "caption_timestamp_order", "previous_start": 2.17, "start": 0, "video_id": "v_-E9YQ_Uhu50"}
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| 4 |
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{"annotation_set": "train", "annotation_source": "ActivityNet Captions", "index": 2, "issue_type": "caption_timestamp_order", "previous_start": 5.54, "start": 0, "video_id": "v_-ew9R4QppTk"}
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| 5 |
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{"annotation_set": "train", "annotation_source": "ActivityNet Captions", "index": 2, "issue_type": "caption_timestamp_order", "previous_start": 8.72, "start": 0, "video_id": "v_07GFy_Z7Nuc"}
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| 6 |
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{"annotation_set": "train", "annotation_source": "ActivityNet Captions", "index": 4, "issue_type": "caption_timestamp_order", "previous_start": 9.33, "start": 6.02, "video_id": "v_07GFy_Z7Nuc"}
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| 7 |
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{"annotation_set": "val_2", "annotation_source": "ActivityNet Captions", "index": 3, "issue_type": "caption_timestamp_order", "previous_start": 14.81, "start": 9.68, "video_id": "v_0JgcRWHCi4c"}
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| 8 |
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{"annotation_set": "train", "annotation_source": "ActivityNet Captions", "index": 2, "issue_type": "caption_timestamp_order", "previous_start": 27.68, "start": 0, "video_id": "v_0ivHmKR8cUw"}
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| 9 |
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{"annotation_set": "train", "annotation_source": "ActivityNet Captions", "index": 1, "issue_type": "caption_timestamp_order", "previous_start": 1.06, "start": 0, "video_id": "v_1BWF1U1dJ_w"}
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| 10 |
+
{"annotation_set": "val_2", "annotation_source": "ActivityNet Captions", "index": 2, "issue_type": "caption_timestamp_order", "previous_start": 1.08, "start": 0, "video_id": "v_1RKExOpIGas"}
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| 11 |
+
{"annotation_set": "train", "annotation_source": "ActivityNet Captions", "index": 2, "issue_type": "caption_timestamp_order", "previous_start": 5.46, "start": 4.1, "video_id": "v_1p_HYu0EZAk"}
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| 12 |
+
{"annotation_set": "train", "annotation_source": "ActivityNet Captions", "index": 2, "issue_type": "caption_timestamp_order", "previous_start": 19.12, "start": 5.09, "video_id": "v_1y9s_l_DIEk"}
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| 13 |
+
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| 258 |
+
{"annotation_set": "activity_net.v1-3", "annotation_source": "ActivityNet v1.3", "duration": 66.64, "index": 0, "issue_type": "action_segment_bounds", "label": "Playing squash", "overshoot_seconds": 0.001290999999994824, "segment": [0, 66.641291], "video_id": "v_rfxXEgc9RTQ"}
|
| 259 |
+
{"annotation_set": "activity_net.v1-3", "annotation_source": "ActivityNet v1.3", "duration": 37.8, "index": 0, "issue_type": "action_segment_bounds", "label": "Putting on makeup", "overshoot_seconds": 0.0020850000000010027, "segment": [1.605866013651518, 37.802085], "video_id": "v_tyuyI30cZ00"}
|
| 260 |
+
{"annotation_set": "activity_net.v1-3", "annotation_source": "ActivityNet v1.3", "duration": 117.05, "index": 0, "issue_type": "action_segment_bounds", "label": "Shaving legs", "overshoot_seconds": 0.001791383219952536, "segment": [0.01, 117.05179138321995], "video_id": "v_v_UmAH95xls"}
|
| 261 |
+
{"annotation_set": "activity_net.v1-3", "annotation_source": "ActivityNet v1.3", "duration": 102.35, "index": 0, "issue_type": "action_segment_bounds", "label": "Drinking coffee", "overshoot_seconds": 0.0035600907029476048, "segment": [71.69539468174727, 102.35356009070294], "video_id": "v_w6P9JrTGRlE"}
|
| 262 |
+
{"annotation_set": "activity_net.v1-3", "annotation_source": "ActivityNet v1.3", "duration": 115.17, "index": 0, "issue_type": "action_segment_bounds", "label": "Using the balance beam", "overshoot_seconds": 0.0009739999999993643, "segment": [14.0018752, 115.170974], "video_id": "v_xYM6h31PrM0"}
|
| 263 |
+
{"annotation_set": "activity_net.v1-3", "annotation_source": "ActivityNet v1.3", "duration": 11.4, "index": 0, "issue_type": "action_segment_bounds", "label": "Playing racquetball", "overshoot_seconds": 0.0009959999999988867, "segment": [0, 11.400996], "video_id": "v_xkIhTMJ_ThA"}
|
| 264 |
+
{"annotation_set": "activity_net.v1-3", "annotation_source": "ActivityNet v1.3", "duration": 36.87, "index": 1, "issue_type": "action_segment_bounds", "label": "Triple jump", "overshoot_seconds": 0.003287000000000262, "segment": [15.9918467800312, 36.873287], "video_id": "v_y9bLCC26MGQ"}
|
| 265 |
+
{"annotation_set": "activity_net.v1-3", "annotation_source": "ActivityNet v1.3", "duration": 19.62, "index": 0, "issue_type": "action_segment_bounds", "label": "Chopping wood", "overshoot_seconds": 0.0008599999999994168, "segment": [0.01, 19.62086], "video_id": "v_ybhc3-s7aYo"}
|
| 266 |
+
{"annotation_set": "activity_net.v1-3", "annotation_source": "ActivityNet v1.3", "duration": 181.51, "index": 0, "issue_type": "action_segment_bounds", "label": "Getting a piercing", "overshoot_seconds": 0.00029478458051812595, "segment": [103.07292870762451, 181.5102947845805], "video_id": "v_yyvc2OSPx1Y"}
|
annotations/eventactivitynet_alignment.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
docs/CHANGELOG.md
CHANGED
|
@@ -24,3 +24,11 @@ Notes:
|
|
| 24 |
- The verified exact Large duration is 106.941600381 hours using `src_fmt_dur`.
|
| 25 |
- The verified class count is 200.
|
| 26 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 24 |
- The verified exact Large duration is 106.941600381 hours using `src_fmt_dur`.
|
| 25 |
- The verified class count is 200.
|
| 26 |
|
| 27 |
+
## v1.0 final small-file update
|
| 28 |
+
|
| 29 |
+
- Added public ActivityNet Captions annotations filtered to the release videos.
|
| 30 |
+
- Added ActivityNet v1.3 action annotations filtered to the release videos.
|
| 31 |
+
- Added EventActivityNet derived caption/action alignment metadata.
|
| 32 |
+
- Added original-rate timing metadata for all 3,263 release videos.
|
| 33 |
+
- Added `metadata/shard_checksums.sha256` with 219 repository-relative tar shard checksums.
|
| 34 |
+
- Updated payload summary metadata with 157 train shards, 62 validation shards, 219 total shards, 3,263 HDF5 members, and 4,355,753,021,440 remote tar bytes.
|
docs/DATASET_CARD.md
CHANGED
|
@@ -139,3 +139,66 @@ Final technical validation confirmed:
|
|
| 139 |
- required datasets and dtypes are present in every file;
|
| 140 |
- Large split counts are 2,316 train and 947 validation;
|
| 141 |
- total release size is approximately 4.36 TB.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 139 |
- required datasets and dtypes are present in every file;
|
| 140 |
- Large split counts are 2,316 train and 947 validation;
|
| 141 |
- total release size is approximately 4.36 TB.
|
| 142 |
+
|
| 143 |
+
## Payload Files
|
| 144 |
+
|
| 145 |
+
The HDF5 payload is distributed as deterministic uncompressed tar shards:
|
| 146 |
+
|
| 147 |
+
- 157 train tar shards under `data/train/`;
|
| 148 |
+
- 62 validation tar shards under `data/validation/`;
|
| 149 |
+
- 219 tar shards total;
|
| 150 |
+
- 3,263 HDF5 members total;
|
| 151 |
+
- one HDF5 member per released ActivityNet video.
|
| 152 |
+
|
| 153 |
+
Large, Medium, and Small share the same physical HDF5 payload. Medium and Small are selected using `scales/medium_ids.txt` and `scales/small_ids.txt`; they do not duplicate payload files.
|
| 154 |
+
|
| 155 |
+
## Annotation Files
|
| 156 |
+
|
| 157 |
+
Additional public annotation files are provided under `annotations/` and `metadata/`:
|
| 158 |
+
|
| 159 |
+
- `annotations/activitynet_captions.json`: ActivityNet Captions timestamped natural-language descriptions for release videos. Validation references preserve `val_1` and `val_2` separately.
|
| 160 |
+
- `annotations/activitynet_actions.json`: ActivityNet v1.3 temporal action segments and labels.
|
| 161 |
+
- `annotations/eventactivitynet_alignment.json`: EventActivityNet project-derived caption/action alignment generated using temporal IoU with midpoint-distance fallback.
|
| 162 |
+
- `annotations/annotation_issues.jsonl`: known upstream annotation quirks recorded without changing canonical values.
|
| 163 |
+
- `metadata/video_metadata.jsonl`: original-rate timing metadata, including exact rational FPS where available.
|
| 164 |
+
|
| 165 |
+
Captions are timestamped descriptions from ActivityNet Captions. They are not instruction-tuning examples.
|
| 166 |
+
|
| 167 |
+
## Timing Metadata
|
| 168 |
+
|
| 169 |
+
EventActivityNet v1.0 uses original-rate, variable-FPS ActivityNet videos. The released HDF5 files were not generated from `anet_240fps_v2` or `anet_240fps_old`.
|
| 170 |
+
|
| 171 |
+
Implementation-derived timing:
|
| 172 |
+
|
| 173 |
+
- one event slice is generated for each adjacent decoded source-frame transition `(e, e + 1)`;
|
| 174 |
+
- one full `events[t]` tensor groups five adjacent-frame transitions and has shape `(5, H, W)`;
|
| 175 |
+
- for source frame count `N`, `events_T = ceil((N - 1) / 5)`;
|
| 176 |
+
- voxel `t` covers event-slice range `[5*t, 5*t + 5)`, clipped to available transitions `[0, N - 1)`;
|
| 177 |
+
- the corresponding source-frame interval is `[5*t, min(5*t + 5, N - 1)]`;
|
| 178 |
+
- with rational FPS `fps_num / fps_den`, the approximate seconds interval is `[5*t * fps_den / fps_num, min(5*t + 5, N - 1) * fps_den / fps_num]`;
|
| 179 |
+
- the final voxel may be partial, with `voxel_event_count[t]` smaller than 5;
|
| 180 |
+
- HDF5 root `fps` is the original source-frame FPS captured by OpenCV, not a 240 fps derivative;
|
| 181 |
+
- `voxel_event_start[t]` is the generated event-slice start index for voxel `t`;
|
| 182 |
+
- `voxel_event_count[t]` is the number of generated adjacent-frame slices in voxel `t`;
|
| 183 |
+
- `voxel_event_start` and `voxel_event_count` are not timestamps and not pixel-event counts.
|
| 184 |
+
|
| 185 |
+
For caption/action interval `[start_seconds, end_seconds]`, use original FPS to compute:
|
| 186 |
+
|
| 187 |
+
```text
|
| 188 |
+
start_frame = floor(start_seconds * fps_num / fps_den)
|
| 189 |
+
end_frame = ceil(end_seconds * fps_num / fps_den)
|
| 190 |
+
t_start = max(0, floor(start_frame / 5))
|
| 191 |
+
t_end_exclusive = min(events_T, ceil(end_frame / 5))
|
| 192 |
+
```
|
| 193 |
+
|
| 194 |
+
Use `[t_start, t_end_exclusive)` for Python slicing, or `[t_start, t_end_exclusive - 1]` as an inclusive range when non-empty. Do not use `time_seconds = t / fps` for voxel starts; voxel start time is approximately `5 * t / fps`.
|
| 195 |
+
|
| 196 |
+
## Checksums
|
| 197 |
+
|
| 198 |
+
Tar shard checksums are published in `metadata/shard_checksums.sha256`. To verify downloaded shards from the repository root:
|
| 199 |
+
|
| 200 |
+
```bash
|
| 201 |
+
sha256sum -c metadata/shard_checksums.sha256
|
| 202 |
+
```
|
| 203 |
+
|
| 204 |
+
The checksum file contains one repository-relative entry for each of the 219 tar shards.
|
docs/DATASET_FORMAT.md
CHANGED
|
@@ -118,3 +118,53 @@ with h5py.File(path, "r") as f:
|
|
| 118 |
|
| 119 |
Avoid loading entire HDF5 arrays into memory unless your system has sufficient RAM.
|
| 120 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 118 |
|
| 119 |
Avoid loading entire HDF5 arrays into memory unless your system has sufficient RAM.
|
| 120 |
|
| 121 |
+
## Payload Shards
|
| 122 |
+
|
| 123 |
+
The HDF5 payload is distributed as deterministic uncompressed tar shards:
|
| 124 |
+
|
| 125 |
+
- 157 train tar shards under `data/train/`;
|
| 126 |
+
- 62 validation tar shards under `data/validation/`;
|
| 127 |
+
- 219 tar shards total;
|
| 128 |
+
- 3,263 HDF5 members total;
|
| 129 |
+
- one HDF5 member per released ActivityNet video.
|
| 130 |
+
|
| 131 |
+
Large, Medium, and Small share the same physical HDF5 payload. Medium and Small are selected using `scales/medium_ids.txt` and `scales/small_ids.txt`; they do not duplicate payload files.
|
| 132 |
+
|
| 133 |
+
## Timing Metadata
|
| 134 |
+
|
| 135 |
+
EventActivityNet v1.0 uses original-rate, variable-FPS ActivityNet videos. The released HDF5 files were not generated from `anet_240fps_v2` or `anet_240fps_old`.
|
| 136 |
+
|
| 137 |
+
Implementation-derived timing:
|
| 138 |
+
|
| 139 |
+
- one event slice is generated for each adjacent decoded source-frame transition `(e, e + 1)`;
|
| 140 |
+
- one full `events[t]` tensor groups five adjacent-frame transitions and has shape `(5, H, W)`;
|
| 141 |
+
- for source frame count `N`, `events_T = ceil((N - 1) / 5)`;
|
| 142 |
+
- voxel `t` covers event-slice range `[5*t, 5*t + 5)`, clipped to available transitions `[0, N - 1)`;
|
| 143 |
+
- the corresponding source-frame interval is `[5*t, min(5*t + 5, N - 1)]`;
|
| 144 |
+
- with rational FPS `fps_num / fps_den`, the approximate seconds interval is `[5*t * fps_den / fps_num, min(5*t + 5, N - 1) * fps_den / fps_num]`;
|
| 145 |
+
- the final voxel may be partial, with `voxel_event_count[t]` smaller than 5;
|
| 146 |
+
- HDF5 root `fps` is the original source-frame FPS captured by OpenCV, not a 240 fps derivative;
|
| 147 |
+
- `voxel_event_start[t]` is the generated event-slice start index for voxel `t`;
|
| 148 |
+
- `voxel_event_count[t]` is the number of generated adjacent-frame slices in voxel `t`;
|
| 149 |
+
- `voxel_event_start` and `voxel_event_count` are not timestamps and not pixel-event counts.
|
| 150 |
+
|
| 151 |
+
For caption/action interval `[start_seconds, end_seconds]`, use original FPS to compute:
|
| 152 |
+
|
| 153 |
+
```text
|
| 154 |
+
start_frame = floor(start_seconds * fps_num / fps_den)
|
| 155 |
+
end_frame = ceil(end_seconds * fps_num / fps_den)
|
| 156 |
+
t_start = max(0, floor(start_frame / 5))
|
| 157 |
+
t_end_exclusive = min(events_T, ceil(end_frame / 5))
|
| 158 |
+
```
|
| 159 |
+
|
| 160 |
+
Use `[t_start, t_end_exclusive)` for Python slicing, or `[t_start, t_end_exclusive - 1]` as an inclusive range when non-empty. Do not use `time_seconds = t / fps` for voxel starts; voxel start time is approximately `5 * t / fps`.
|
| 161 |
+
|
| 162 |
+
## Checksums
|
| 163 |
+
|
| 164 |
+
Tar shard checksums are published in `metadata/shard_checksums.sha256`. To verify downloaded shards from the repository root:
|
| 165 |
+
|
| 166 |
+
```bash
|
| 167 |
+
sha256sum -c metadata/shard_checksums.sha256
|
| 168 |
+
```
|
| 169 |
+
|
| 170 |
+
The checksum file contains one repository-relative entry for each of the 219 tar shards.
|
docs/DATASET_GENERATION.md
CHANGED
|
@@ -99,3 +99,44 @@ Final validation confirmed:
|
|
| 99 |
- zero truncated or unreadable files;
|
| 100 |
- zero remaining structural warnings;
|
| 101 |
- Large train/validation split: 2,316 / 947.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 99 |
- zero truncated or unreadable files;
|
| 100 |
- zero remaining structural warnings;
|
| 101 |
- Large train/validation split: 2,316 / 947.
|
| 102 |
+
|
| 103 |
+
## Original-Rate Timing Mapping
|
| 104 |
+
|
| 105 |
+
EventActivityNet v1.0 uses original-rate, variable-FPS ActivityNet videos. The released HDF5 files were not generated from `anet_240fps_v2` or `anet_240fps_old`.
|
| 106 |
+
|
| 107 |
+
Implementation-derived timing:
|
| 108 |
+
|
| 109 |
+
- one event slice is generated for each adjacent decoded source-frame transition `(e, e + 1)`;
|
| 110 |
+
- one full `events[t]` tensor groups five adjacent-frame transitions and has shape `(5, H, W)`;
|
| 111 |
+
- for source frame count `N`, `events_T = ceil((N - 1) / 5)`;
|
| 112 |
+
- voxel `t` covers event-slice range `[5*t, 5*t + 5)`, clipped to available transitions `[0, N - 1)`;
|
| 113 |
+
- the corresponding source-frame interval is `[5*t, min(5*t + 5, N - 1)]`;
|
| 114 |
+
- with rational FPS `fps_num / fps_den`, the approximate seconds interval is `[5*t * fps_den / fps_num, min(5*t + 5, N - 1) * fps_den / fps_num]`;
|
| 115 |
+
- the final voxel may be partial, with `voxel_event_count[t]` smaller than 5;
|
| 116 |
+
- HDF5 root `fps` is the original source-frame FPS captured by OpenCV, not a 240 fps derivative;
|
| 117 |
+
- `voxel_event_start[t]` is the generated event-slice start index for voxel `t`;
|
| 118 |
+
- `voxel_event_count[t]` is the number of generated adjacent-frame slices in voxel `t`;
|
| 119 |
+
- `voxel_event_start` and `voxel_event_count` are not timestamps and not pixel-event counts.
|
| 120 |
+
|
| 121 |
+
For caption/action interval `[start_seconds, end_seconds]`, use original FPS to compute:
|
| 122 |
+
|
| 123 |
+
```text
|
| 124 |
+
start_frame = floor(start_seconds * fps_num / fps_den)
|
| 125 |
+
end_frame = ceil(end_seconds * fps_num / fps_den)
|
| 126 |
+
t_start = max(0, floor(start_frame / 5))
|
| 127 |
+
t_end_exclusive = min(events_T, ceil(end_frame / 5))
|
| 128 |
+
```
|
| 129 |
+
|
| 130 |
+
Use `[t_start, t_end_exclusive)` for Python slicing, or `[t_start, t_end_exclusive - 1]` as an inclusive range when non-empty. Do not use `time_seconds = t / fps` for voxel starts; voxel start time is approximately `5 * t / fps`.
|
| 131 |
+
|
| 132 |
+
## Annotation Alignment
|
| 133 |
+
|
| 134 |
+
Additional public annotation files are provided under `annotations/` and `metadata/`:
|
| 135 |
+
|
| 136 |
+
- `annotations/activitynet_captions.json`: ActivityNet Captions timestamped natural-language descriptions for release videos. Validation references preserve `val_1` and `val_2` separately.
|
| 137 |
+
- `annotations/activitynet_actions.json`: ActivityNet v1.3 temporal action segments and labels.
|
| 138 |
+
- `annotations/eventactivitynet_alignment.json`: EventActivityNet project-derived caption/action alignment generated using temporal IoU with midpoint-distance fallback.
|
| 139 |
+
- `annotations/annotation_issues.jsonl`: known upstream annotation quirks recorded without changing canonical values.
|
| 140 |
+
- `metadata/video_metadata.jsonl`: original-rate timing metadata, including exact rational FPS where available.
|
| 141 |
+
|
| 142 |
+
Captions are timestamped descriptions from ActivityNet Captions. They are not instruction-tuning examples.
|
docs/LICENSE_NOTES.md
CHANGED
|
@@ -35,3 +35,7 @@ If redistributing EventActivityNet or derived files:
|
|
| 35 |
The source videos are public web videos included in ActivityNet. EventActivityNet v1.0 is intended for research use, not for identifying people, surveillance, biometric recognition, or consequential decision-making about individuals.
|
| 36 |
|
| 37 |
The final release audit found no remaining HDF5 integrity problems. Public documentation should continue to avoid local filesystem paths and unrelated private metadata.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 35 |
The source videos are public web videos included in ActivityNet. EventActivityNet v1.0 is intended for research use, not for identifying people, surveillance, biometric recognition, or consequential decision-making about individuals.
|
| 36 |
|
| 37 |
The final release audit found no remaining HDF5 integrity problems. Public documentation should continue to avoid local filesystem paths and unrelated private metadata.
|
| 38 |
+
|
| 39 |
+
## Annotation Attribution
|
| 40 |
+
|
| 41 |
+
ActivityNet Captions provides the timestamped natural-language descriptions. ActivityNet v1.3 provides the temporal action segments and labels. EventActivityNet provides derived event voxel tensors and the project-derived caption/action alignment metadata. These derived files do not grant additional rights beyond the original ActivityNet and ActivityNet Captions terms.
|
docs/RELEASE_NOTES.md
CHANGED
|
@@ -61,3 +61,52 @@ Estimated shard counts from the publication audit:
|
|
| 61 |
- 20 GB target: about 218 shards.
|
| 62 |
- 50 GB target: about 87 shards.
|
| 63 |
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|
| 61 |
- 20 GB target: about 218 shards.
|
| 62 |
- 50 GB target: about 87 shards.
|
| 63 |
|
| 64 |
+
## Final Payload Packaging
|
| 65 |
+
|
| 66 |
+
- 157 train tar shards.
|
| 67 |
+
- 62 validation tar shards.
|
| 68 |
+
- 219 total tar shards.
|
| 69 |
+
- 3,263 HDF5 members.
|
| 70 |
+
- Total remote tar bytes: 4,355,753,021,440.
|
| 71 |
+
- Final shard checksums are published in `metadata/shard_checksums.sha256`.
|
| 72 |
+
|
| 73 |
+
## Final Annotation Metadata
|
| 74 |
+
|
| 75 |
+
Additional public annotation files are provided under `annotations/` and `metadata/`:
|
| 76 |
+
|
| 77 |
+
- `annotations/activitynet_captions.json`: ActivityNet Captions timestamped natural-language descriptions for release videos. Validation references preserve `val_1` and `val_2` separately.
|
| 78 |
+
- `annotations/activitynet_actions.json`: ActivityNet v1.3 temporal action segments and labels.
|
| 79 |
+
- `annotations/eventactivitynet_alignment.json`: EventActivityNet project-derived caption/action alignment generated using temporal IoU with midpoint-distance fallback.
|
| 80 |
+
- `annotations/annotation_issues.jsonl`: known upstream annotation quirks recorded without changing canonical values.
|
| 81 |
+
- `metadata/video_metadata.jsonl`: original-rate timing metadata, including exact rational FPS where available.
|
| 82 |
+
|
| 83 |
+
Captions are timestamped descriptions from ActivityNet Captions. They are not instruction-tuning examples.
|
| 84 |
+
|
| 85 |
+
## Timing Clarification
|
| 86 |
+
|
| 87 |
+
EventActivityNet v1.0 uses original-rate, variable-FPS ActivityNet videos. The released HDF5 files were not generated from `anet_240fps_v2` or `anet_240fps_old`.
|
| 88 |
+
|
| 89 |
+
Implementation-derived timing:
|
| 90 |
+
|
| 91 |
+
- one event slice is generated for each adjacent decoded source-frame transition `(e, e + 1)`;
|
| 92 |
+
- one full `events[t]` tensor groups five adjacent-frame transitions and has shape `(5, H, W)`;
|
| 93 |
+
- for source frame count `N`, `events_T = ceil((N - 1) / 5)`;
|
| 94 |
+
- voxel `t` covers event-slice range `[5*t, 5*t + 5)`, clipped to available transitions `[0, N - 1)`;
|
| 95 |
+
- the corresponding source-frame interval is `[5*t, min(5*t + 5, N - 1)]`;
|
| 96 |
+
- with rational FPS `fps_num / fps_den`, the approximate seconds interval is `[5*t * fps_den / fps_num, min(5*t + 5, N - 1) * fps_den / fps_num]`;
|
| 97 |
+
- the final voxel may be partial, with `voxel_event_count[t]` smaller than 5;
|
| 98 |
+
- HDF5 root `fps` is the original source-frame FPS captured by OpenCV, not a 240 fps derivative;
|
| 99 |
+
- `voxel_event_start[t]` is the generated event-slice start index for voxel `t`;
|
| 100 |
+
- `voxel_event_count[t]` is the number of generated adjacent-frame slices in voxel `t`;
|
| 101 |
+
- `voxel_event_start` and `voxel_event_count` are not timestamps and not pixel-event counts.
|
| 102 |
+
|
| 103 |
+
For caption/action interval `[start_seconds, end_seconds]`, use original FPS to compute:
|
| 104 |
+
|
| 105 |
+
```text
|
| 106 |
+
start_frame = floor(start_seconds * fps_num / fps_den)
|
| 107 |
+
end_frame = ceil(end_seconds * fps_num / fps_den)
|
| 108 |
+
t_start = max(0, floor(start_frame / 5))
|
| 109 |
+
t_end_exclusive = min(events_T, ceil(end_frame / 5))
|
| 110 |
+
```
|
| 111 |
+
|
| 112 |
+
Use `[t_start, t_end_exclusive)` for Python slicing, or `[t_start, t_end_exclusive - 1]` as an inclusive range when non-empty. Do not use `time_seconds = t / fps` for voxel starts; voxel start time is approximately `5 * t / fps`.
|
metadata/shard_checksums.sha256
ADDED
|
@@ -0,0 +1,219 @@
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metadata/shard_summary.json
CHANGED
|
@@ -1,4 +1,6 @@
|
|
| 1 |
{
|
|
|
|
|
|
|
| 2 |
"dataset_repo_id": "IIS-CVL/EventActivityNet",
|
| 3 |
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| 4 |
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|
|
@@ -27,7 +29,9 @@
|
|
| 27 |
"median": 19980668969,
|
| 28 |
"min": 2530106597
|
| 29 |
},
|
|
|
|
| 30 |
"planned_h5_count": 3263,
|
|
|
|
| 31 |
"planned_shards_larger_than_hard_cap": [
|
| 32 |
{
|
| 33 |
"allowed_reason": "single H5 exceeds hard cap",
|
|
@@ -61,8 +65,14 @@
|
|
| 61 |
"total_size_bytes": 1221206599097
|
| 62 |
}
|
| 63 |
},
|
| 64 |
-
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|
| 65 |
"target_shard_size_bytes": 20000000000,
|
|
|
|
|
|
|
| 66 |
"total_payload_size_bytes": 4355745895245,
|
| 67 |
-
"
|
|
|
|
|
|
|
|
|
|
|
|
|
| 68 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"actual_remote_tar_size_bytes": 4355753021440,
|
| 3 |
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"checksum_file": "metadata/shard_checksums.sha256",
|
| 4 |
"dataset_repo_id": "IIS-CVL/EventActivityNet",
|
| 5 |
"expected_h5_count": 3263,
|
| 6 |
"hard_shard_size_cap_bytes": 22000000000,
|
|
|
|
| 29 |
"median": 19980668969,
|
| 30 |
"min": 2530106597
|
| 31 |
},
|
| 32 |
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"payload_lineage": "original-rate variable-FPS",
|
| 33 |
"planned_h5_count": 3263,
|
| 34 |
+
"planned_member_count": 3263,
|
| 35 |
"planned_shards_larger_than_hard_cap": [
|
| 36 |
{
|
| 37 |
"allowed_reason": "single H5 exceeds hard cap",
|
|
|
|
| 65 |
"total_size_bytes": 1221206599097
|
| 66 |
}
|
| 67 |
},
|
| 68 |
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"status": "final_verified_release",
|
| 69 |
"target_shard_size_bytes": 20000000000,
|
| 70 |
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"total_h5_payload_bytes": 4355745895245,
|
| 71 |
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"total_members": 3263,
|
| 72 |
"total_payload_size_bytes": 4355745895245,
|
| 73 |
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"total_remote_tar_bytes": 4355753021440,
|
| 74 |
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"total_shard_count": 219,
|
| 75 |
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"total_shards": 219,
|
| 76 |
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"train_shards": 157,
|
| 77 |
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"validation_shards": 62
|
| 78 |
}
|
metadata/video_metadata.jsonl
ADDED
|
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|
|
|