| # SA-Co/VEval Dataset |
| **License** each domain has its own License |
| * SA-Co/VEval - SA-V: CC-BY-NC 4.0 |
| * SA-Co/VEval - YT-Temporal-1B: CC-BY-NC 4.0 |
| * SA-Co/VEval - SmartGlasses: CC-by-4.0 |
|
|
| **SA-Co/VEval** is an evaluation dataset comprising of 3 domains, each domain has a val and test split. |
| * SA-Co/VEval - SA-V: videos are from the [SA-V dataset](https://ai.meta.com/datasets/segment-anything-video/) |
| * SA-Co/VEval - YT-Temporal-1B: videos are from the [YT-Temporal-1B](https://cove.thecvf.com/datasets/704) |
| * SA-Co/VEval - SmartGlasses: egocentric videos from [Smart Glasses](https://huggingface.co/datasets/facebook/SACo-VEval/blob/main/media/saco_sg.tar.gz) |
|
|
| ## Environment |
| Install the SA-Co/VEVal required environment |
| ``` |
| pip install -e ".[veval]" |
| ``` |
| This will allow us to run: |
| * `scripts/eval/veval/saco_yt1b_downloader.py` preparing frames for SA-Co/VEval - YT-Temporal-1B |
| * `examples/saco_veval_eval_example.ipynb` example of running an offline evaluator |
| * `examples/saco_veval_vis_example.ipynb` example of loading and visualizing the data |
|
|
| ## Download |
| ### The expected folder structure |
| The following folder structure is expected after finishing all the download and pre-processing steps in this section |
| ``` |
| data/ |
| ├── annotation/ |
| │ ├── saco_veval_sav_test.json |
| │ ├── saco_veval_sav_val.json |
| │ ├── saco_veval_smartglasses_test.json |
| │ ├── saco_veval_smartglasses_val.json |
| │ ├── saco_veval_yt1b_test.json |
| │ ├── saco_veval_yt1b_val.json |
| └── media/ |
| ├── saco_sav |
| │ └── JPEGImages_24fps |
| ├── saco_sg |
| │ └── JPEGImages_6fps |
| └── saco_yt1b |
| └── JPEGImages_6fps |
| ``` |
| ### Download ready-to-use data |
| The following links provide ready-to-use data, hosted on Roboflow, after completing the pre-processing steps outlined in the next section. |
|
|
| For each domain: |
| - [SA-Co/VEval - SA-V](https://universe.roboflow.com/sa-co-veval/sa-v-test/) |
| - [SA-Co/VEval - YT-Temporal-1B](https://universe.roboflow.com/sa-co-veval/yt-temporal-1b-test/) |
| - [SA-Co/VEval - SmartGlasses](https://universe.roboflow.com/sa-co-veval/smartglasses-test/) |
|
|
| For all three domains: |
| - [SA-Co/VEval](https://universe.roboflow.com/sa-co-veval) |
|
|
| Special note on **SA-Co/VEval - YT-Temporal-1B**: |
| * **Frame Shifting Alert!** |
| * The ready-to-use data hosted on Roboflow was produced by following the preprocessing steps below. Therefore, the frame-shifting issue for YT-Temporal-1B still exists: due to the nature of Youtube videos, the re-downloaded videos may not be exactly the same as those used during annotation, which can affect eval number reproducibility. |
|
|
| ### Download via preprocessing steps |
| #### Download annotations |
| The GT annotations are available at Hugging Face: |
| * [SA-Co/VEval](https://huggingface.co/datasets/facebook/SACo-VEval/tree/main) |
| * SA-Co/VEval SA-V |
| * Test: `annotation/saco_veval_sav_test.json` |
| * Val: `annotation/saco_veval_sav_val.json` |
| * SA-Co/VEval YT-Temporal-1B |
| * Test: `annotation/saco_veval_yt1b_test.json` |
| * Val: `annotation/saco_veval_yt1b_val.json` |
| * SA-Co/VEval SmartGlasses |
| * Test: `annotation/saco_veval_smartglasses_test.json` |
| * Val: `annotation/saco_veval_smartglasses_val.json` |
|
|
| #### Download videos or frames |
| ##### SA-Co/VEval - SAV |
| Follow instructions in [SA-V dataset](https://ai.meta.com/datasets/segment-anything-video/). Only the following two datasets are needed: |
| * sav_test.tar |
| * sav_val.tar |
|
|
| After untar: |
| ``` |
| sav_test/ |
| ├── Annotations_6fps [ignore this is the SAM 2 annotation] |
| ├── JPEGImages_24fps |
| sav_val/ |
| ├── Annotations_6fps [ignore this is the SAM 2 annotation] |
| └── JPEGImages_24fps |
| ``` |
| Then merge the two JPEGImages_24fps together to better match our annotation json file path e.g. |
| ``` |
| media/ |
| └── saco_sav |
| └── JPEGImages_24fps [merged from the two JPEGImages_24fps above] |
| ``` |
| Example commands to download and merge folders |
| ``` |
| cd ../data/media/saco_sav |
| wget -O sav_test.tar <sav_test.tar download link from the SA-V dataset page> |
| wget -O sav_val.tar <sav_val.tar download link from the SA-V dataset page> |
| tar -xf sav_test.tar |
| tar -xf sav_val.tar |
| mkdir JPEGImages_24fps |
| chmod -R u+w sav_test/ |
| chmod -R u+w sav_val/ |
| mv sav_test/JPEGImages_24fps/* JPEGImages_24fps/ |
| mv sav_val/JPEGImages_24fps/* JPEGImages_24fps/ |
| ``` |
| |
| ##### SA-Co/VEval - YT-Temporal-1B |
| Two files are needed to download the SA-Co/VEval - YT-Temporal-1B Youtube videos. |
| * Download `media/yt1b_start_end_time.json` from [SA-Co/VEval](https://huggingface.co/datasets/facebook/SACo-VEval/tree/main), which contains the Youtube video ids and the start and end time used in SA-Co/VEval - YT-Temporal-1B. |
| * Prepare the `cookies.txt` file. Follow instruction in yt-dlp [exporting-youtube-cookies](https://github.com/yt-dlp/yt-dlp/wiki/Extractors#exporting-youtube-cookies) and [pass-cookies-to-yt-dlp](https://github.com/yt-dlp/yt-dlp/wiki/FAQ#how-do-i-pass-cookies-to-yt-dlp) to prepare the cookies_file. |
| * Please see the full **WARNINGS** in yt-dlp regarding the risk of Youtube account ban!! |
| |
| Then run `scripts/eval/veval/saco_yt1b_downloader.py` to download the videos and prepare the frames e.g. |
| ``` |
| python saco_yt1b_downloader.py \ |
| --data_dir ../data/media/saco_yt1b \ |
| --cookies_file ../data/media/saco_yt1b/cookies.txt \ |
| --yt1b_start_end_time_file ../data/media/saco_yt1b/yt1b_start_end_time.json \ |
| --yt1b_frame_prep_log_file ../data/media/saco_yt1b/yt1b_frame_prep.log |
| ``` |
| * data_dir: The directoy to download the Youtube videos and store the extraced frames |
| * cookies_file: the `cookies.txt` downloaded above |
| * yt1b_start_end_time_file: the `yt1b_start_end_time.json` downloaded above |
| * yt1b_frame_prep_log_file: a log file to track the video downloading and frame extracting status |
| |
| Then run `scripts/eval/veval/saco_yt1b_annot_update.py` to update the annotation based on the video availability e.g. |
| ``` |
| python saco_yt1b_annot_update.py \ |
| --yt1b_media_dir ../data/media/saco_yt1b/JPEGImages_6fps \ |
| --yt1b_input_annot_path ../data/annotation/saco_veval_yt1b_val.json \ |
| --yt1b_output_annot_path ../data/annotation/saco_veval_yt1b_val_updated.json \ |
| --yt1b_annot_update_log_path ../data/annotation/saco_veval_yt1b_val_updated.log |
| ``` |
| |
| **NOTE**: |
| * Not all Youtube videos might be available as Youtube videos can be deleted or become private. The script `saco_yt1b_annot_update.py` is used to remove the annotations of the unavailable videos. |
| * **Frame Shifting Alert!!** Even when the videos are still available, their specifications, such as fps and duration, may differ from those used during annotation when re-downloaded from YouTube. Additionally, sometimes `ffmpeg` seems to find it hard to guarantee consistent frame extraction from the same video across different environments. This may cause the re-downloaded and re-extracted frames to have alignment issues with our annotations due to frame shifting. Please be aware of this caveat when evaluating on SA-Co/VEval - YT-Temporal-1B. |
| |
| ##### SA-Co/VEval - SmartGlasses |
| Go to [SACo-VEval](https://huggingface.co/datasets/facebook/SACo-VEval/tree/main) download `media/saco_sg.tar.gz` |
| ``` |
| cd ../data |
| hf download facebook/SACo-VEval media/saco_sg.tar.gz --repo-type dataset --local-dir . |
| cd ../data/media |
| tar -xzf saco_sg.tar.gz |
| ``` |
| |
| ## Annotation Format |
| The format is similar to the [YTVIS](https://youtube-vos.org/dataset/vis/) format. |
| |
| In the annotation json, e.g. `saco_veval_sav_test.json` there are 5 fields: |
| * info: |
| * A dict containing the dataset info |
| * E.g. {'version': 'v1', 'date': '2025-09-24', 'description': 'SA-Co/VEval SA-V Test'} |
| * videos |
| * A list of videos that are used in the current annotation json |
| * It contains {id, video_name, file_names, height, width, length} |
| * annotations |
| * A list of **positive** masklets and their related info |
| * It contains {id, segmentations, bboxes, areas, iscrowd, video_id, height, width, category_id, noun_phrase} |
| * video_id should match to the `videos - id` field above |
| * category_id should match to the `categories - id` field below |
| * segmentations is a list of [RLE](https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/mask.py) |
| * categories |
| * A **globally** used noun phrase id map, which is true across all 3 domains. |
| * It contains {id, name} |
| * name is the noun phrase |
| * video_np_pairs |
| * A list of video-np pairs, including both **positive** and **negative** used in the current annotation json |
| * It contains {id, video_id, category_id, noun_phrase, num_masklets} |
| * video_id should match the `videos - id` above |
| * category_id should match the `categories - id` above |
| * when `num_masklets > 0` it is a positive video-np pair, and the presenting masklets can be found in the annotations field |
| * when `num_masklets = 0` it is a negative video-np pair, meaning no masklet presenting at all |
| ``` |
| data { |
| "info": info |
| "videos": [video] |
| "annotations": [annotation] |
| "categories": [category] |
| "video_np_pairs": [video_np_pair] |
| } |
| video { |
| "id": int |
| "video_name": str # e.g. sav_000000 |
| "file_names": List[str] |
| "height": int |
| "width": width |
| "length": length |
| } |
| annotation { |
| "id": int |
| "segmentations": List[RLE] |
| "bboxes": List[List[int, int, int, int]] |
| "areas": List[int] |
| "iscrowd": int |
| "video_id": str |
| "height": int |
| "width": int |
| "category_id": int |
| "noun_phrase": str |
| } |
| category { |
| "id": int |
| "name": str |
| } |
| video_np_pair { |
| "id": int |
| "video_id": str |
| "category_id": int |
| "noun_phrase": str |
| "num_masklets" int |
| } |
| ``` |
| [sam3/examples/saco_veval_vis_example.ipynb](https://github.com/facebookresearch/sam3/blob/main/examples/saco_veval_vis_example.ipynb) shows some examples of the data format and data visualization. |
| |
| ## Run Offline Eval |
| An example notebook and an eval script have been provided for offline evaluation. |
| ``` |
| sam3/ |
| ├── examples/ |
| │ └── saco_veval_eval_example.ipynb # this notebook will load eval res or run the eval on the fly, and print the results |
| └── sam3/eval/ |
| └── saco_veval_eval.py # this script will run the offline evaluator |
| ``` |
| `saco_veval_eval.py` supports two modes, `one` and `all`. |
| * `one`: will take only one pair of gt and pred files to eval |
| * `all`: will eval on all 6 SACo/VEval datasets |
|
|
| Example usage |
| ``` |
| python saco_veval_eval.py one \ |
| --gt_annot_file ../sam3/assets/veval/toy_gt_and_pred/toy_saco_veval_sav_test_gt.json \ |
| --pred_file ../sam3/assets/veval/toy_gt_and_pred/toy_saco_veval_sav_test_pred.json \ |
| --eval_res_file ../sam3/assets/veval/toy_gt_and_pred/toy_saco_veval_sav_test_eval_res.json |
| ``` |
| * `gt_annot_file`: the location of the GT file |
| * `pred_file`: the location of the Pred file |
| * `eval_res_file`: the location where the eval result will be written to |
|
|
| ``` |
| python saco_veval_eval.py all \ |
| --gt_annot_dir ../data/annotation \ |
| --pred_dir ../data/pred \ |
| --eval_res_dir ../data/pred |
| ``` |
| * `gt_annot_dir`: the location of the GT files |
| * `pred_dir`: the location of the Pred files |
| * `eval_res_dir`: the location where the eval results will be written to |
|
|
