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SA-Co/Silver benchmark

SA-Co/Silver is a benchmark for promptable concept segmentation (PCS) in images. The benchmark contains images paired with text labels (also referred as Noun Phrases aka NPs), each annotated exhaustively with masks on all object instances that match the label.

SA-Co/Silver comprises 10 subsets, covering a diverse array of domains including food, art, robotics, driving etc. Unlike SA-Co/Gold, there is only a single ground-truth for each datapoint, which means the results may have a bit more variance and tend to underestimate model performance, since they don't account for possible different interpretations of each query.

  • BDD100k
  • DROID
  • Ego4D
  • MyFoodRepo-273
  • GeoDE
  • iNaturalist-2017
  • National Gallery of Art
  • SA-V
  • YT-Temporal-1B
  • Fathomnet

The README contains instructions on how to download and setup the annotations, image data to prepare them for evaluation on SA-Co/Silver.

Preparation

Download annotations

The GT annotations can be downloaded from Hugging Face or Roboflow

Download images and video frames

Image Datasets

GeoDE

The processed images needed for evaluation can be downloaded from Roboflow OR follow the below steps to prepare the processed images.

  1. Download dataset with raw images from GeoDE.

  2. Extract the downloaded file to a location, say <RAW_GEODE_IMAGES_FOLDER>

  3. Run the below command to pre-process the images and prepare for evaluation. The proceesed images will be saved to the location specified in <PROCESSED_GEODE_IMAGES_FOLDER>

    python preprocess_silver_geode_bdd100k_food_rec.py --annotation_file <FOLDER_WITH_SILVER_ANNOTATIONS>/silver_geode_merged_test.json --raw_images_folder <RAW_GEODE_IMAGES_FOLDER> --processed_images_folder <PROCESSED_GEODE_IMAGES_FOLDER> --dataset_name geode

National Gallery of Art (NGA)

The processed images needed for evaluation can be downloaded from Roboflow OR follow the below steps to prepare the processed images.

  1. Run the below command to download raw images and pre-process the images to prepare for evaluation. The proceesed images will be saved to the location specified in <PROCESSED_NGA_IMAGES_FOLDER>.
    python download_preprocess_nga.py --annotation_file <FOLDER_WITH_SILVER_ANNOTATIONS>/silver_nga_art_merged_test.json --raw_images_folder <RAW_NGA_IMAGES_FOLDER> --processed_images_folder <PROCESSED_NGA_IMAGES_FOLDER>

Berkeley Driving Dataset (BDD) 100k

The processed images needed for evaluation can be downloaded from Roboflow OR follow the below steps to prepare the processed images.

  1. Download data with raw images from the 100K Images dataset in BDD100k
  2. Extract the downloaded file to a location, say <RAW_BDD_IMAGES_FOLDER>
  3. Run the below command to pre-process the images and prepare for evaluation. The proceesed images will be saved to the location specified in <PROCESSED_BDD_IMAGES_FOLDER>
    python preprocess_silver_geode_bdd100k_food_rec.py --annotation_file <FOLDER_WITH_SILVER_ANNOTATIONS>/silver_bdd100k_merged_test.json --raw_images_folder <RAW_BDD_IMAGES_FOLDER> --processed_images_folder <PROCESSED_BDD_IMAGES_FOLDER> --dataset_name bdd100k

Food Recognition Challenge 2022

  1. Download data with raw images from the website. Download [Round 2] public_validation_set_2.0.tar.gz file.
  2. Extract the downloaded file to a location, say <RAW_FOOD_IMAGES_FOLDER>
  3. Run the below command to pre-process the images and prepare for evaluation. The proceesed images will be saved to the location specified in <PROCESSED_FOOD_IMAGES_FOLDER>
    python preprocess_silver_geode_bdd100k_food_rec.py --annotation_file <FOLDER_WITH_SILVER_ANNOTATIONS>/silver_food_rec_merged_test.json --raw_images_folder <RAW_FOOD_IMAGES_FOLDER> --processed_images_folder <PROCESSED_FOOD_IMAGES_FOLDER> --dataset_name food_rec

iNaturalist

The processed images needed for evaluation can be downloaded from Roboflow OR follow the below steps to prepare the processed images.

  1. Run the below command to download, extract images in <RAW_INATURALIST_IMAGES_FOLDER> and prepare them for evaluation. The proceesed images will be saved to the location specified in <PROCESSED_INATURALIST_IMAGES_FOLDER>
    python download_inaturalist.py --raw_images_folder <RAW_INATURALIST_IMAGES_FOLDER> --processed_images_folder <PROCESSED_INATURALIST_IMAGES_FOLDER>

Fathomnet

The processed images needed for evaluation can be downloaded from Roboflow OR follow the below steps to prepare the processed images.

  1. Install the FathomNet API

    pip install fathomnet

  2. Run the below command to download the images and prepare for evaluation. The proceesed images will be saved to the location specified in <PROCESSED_BDD_IMAGES_FOLDER>

    python download_fathomnet.py --processed_images_folder <PROCESSED_BFATHOMNET_IMAGES_FOLDER>

Frame Datasets

These datasets correspond to annotations for individual frames coming from videos. The file CONFIG_FRAMES.yaml is used to unify the downloads for the datasets, as explained below.

Before following the other dataset steps, update CONFIG_FRAMES.yaml with the correct path_annotations path where the annotation files are.

DROID

The processed frames needed for evaluation can be downloaded from Roboflow OR follow the below steps to prepare the processed frames.

  1. Install the gsutil package:
    pip install gsutil
  2. Modify the droid_path variable in CONFIG_FRAMES.yaml. This is the path where the DROID data will be downloaded.
  3. _[Optional] Update the variable remove_downloaded_videos_droid to (not) remove the videos after the frames have been extracted.
  4. Download the data:
    python download_videos.py droid
  5. Extract the frames:
    python extract_frames.py droid

See the DROID website for more information.

SA-V

The processed frames needed for evaluation can be downloaded from Roboflow OR follow the below steps to prepare the processed frames.

  1. Follow instructions in the Segment Anything official website to obtain access to the download links (they are dynamic links).
  2. Update CONFIG_FRAMES.yaml:
    • Update the sav_path variable, where the frames will be saved.
    • Update the sav_videos_fps_6_download_path variable. Copy paste the path corresponding to the videos_fps_6.tar in the list that you obtained in step 1.
    • [Optional] Update the variable remove_downloaded_videos_sav to (not) remove the videos after the frames have been extracted.
  3. Download the videos:
    python download_videos.py sav
  4. Extract the frames:
    python extract_frames.py sav

Ego4D

The processed frames needed for evaluation can be downloaded from Roboflow OR follow the below steps to prepare the processed frames.

  1. Review and accept the license agreement in the official Ego4D website.
  2. Configure AWS credentials. Run:
    pip install awscli
aws configure
    and copy the values shown in the email you received after step 1 (you can leave "region name" and "output format" empty). You can verify that the variables were set up correctly:
    cat ~/.aws/credentials
  3. Install the Ego4D library:
    pip install ego4d
  4. Update CONFIG_FRAMES.yaml:
    • Set up AWS credentials following the instructions in the email you received after step 2. Modify the following variables: aws_access_key_id and aws_secret_access_key.
    • Update the ego4d_path variable, where the frames will be saved.
    • [Optional] Update the variable remove_downloaded_videos_ego4d to (not) remove the videos after the frames have been extracted..
  5. Download the clips subset of the Ego4D dataset:
    python download_videos.py ego4d
  6. Extract the frames:
    python extract_frames.py ego4d

See the official CLI and the explanation about the videos for more information.

YT1B

The processed frames needed for evaluation can be downloaded from Roboflow OR follow the below steps to prepare the processed frames.

  1. Install the yt-dlp library:
    python3 -m pip install -U "yt-dlp[default]"
  2. Create a cookies.txt file following the instructions from yt-dlp exporting-youtube-cookies and pass-cookies-to-yt-dlp. This is required to download youtube videos. Then, update the path for that file in the CONFIG_FRAMES.yaml file, in the variable cookies_path.
  3. Update CONFIG_FRAMES.yaml:
    • Update the yt1b_path, where the frames will be saved.
    • [Optional] Some YouTube videos may not be available on YouTube anymore. Set update_annotation_yt1b to True in CONFIG_FRAMES.yaml to remove the annotations corresponding to such videos. Note that the evaluations will not be directly comparable with other reported evaluations.
    • [Optional] Update the variable remove_downloaded_videos_yt1b to (not) remove the videos after the frames have been extracted.
  4. Run the following code to download the videos:
    python download_videos.py yt1b
  5. Extract the frames:
    python extract_frames.py yt1b

Usage

Visualization

Run evaluation

The official metric for SA-Co/Silver is cgF1. Please refer to the SAM3 paper for details. Unlike Gold, the silver subset only has a single annotation per image. Therefore, the performance may be underestimated, because the model may be wrongly penalized for choosing an interpretation which is valid but different from that of the human annotator.

Evaluate SAM3

We provide inference configurations to reproduce the evaluation of SAM3. First, please edit the file eval_base.yaml with the paths where you downloaded the images and annotations above.

There are 10 subsets and as many configurations to be run. Let's take the first subset as an example. The inference can be run locally using the following command (you can adjust the number of gpus):

python sam3/train/train.py -c configs/silver_image_evals/sam3_gold_image_bdd100k.yaml --use-cluster 0 --num-gpus 1

The predictions will be dumped in the folder specified in eval_base.yaml.

We also provide support for SLURM-based cluster inference. Edit the eval_base.yaml file to reflect your slurm configuration (partition, qos, ...), then run

python sam3/train/train.py -c configs/silver_image_evals/sam3_gold_image_bdd100k.yaml --use-cluster 1

Offline evaluation

If you have the predictions in the COCO result format (see here), then we provide scripts to easily run the evaluation.

For an example on how to run the evaluator on all subsets and aggregate results, see the following notebook: saco_gold_silver_eval_example.ipynb

If you have a prediction file for a given subset, you can run the evaluator specifically for that one using the standalone script. Example:

python scripts/eval/standalone_cgf1.py --pred_file /path/to/coco_predictions_segm.json --gt_files /path/to/annotations/silver_bdd100k_merged_test.json

Results

Average BDD100k Droids Ego4d Food Rec Geode iNaturalist Nga Art SAV YT1B Fathomnet
cgF1 IL_MCC PmF1 CGF1 IL_MCC pmF1 CGF1 IL_MCC pmF1 CGF1 IL_MCC pmF1 CGF1 IL_MCC pmF1 CGF1 IL_MCC pmF1 CGF1 IL_MCC pmF1 CGF1 IL_MCC pmF1 CGF1 IL_MCC pmF1 CGF1 IL_MCC pmF1 CGF1 IL_MCC pmF1
gDino-T 3.09 0.12 19.75 3.33 0.17 19.54 4.26 0.15 28.38 2.87 0.1 28.72 0.69 0.05 13.88 9.61 0.24 40.03 0 0 1.97 1.31 0.09 14.57 5.18 0.19 27.25 3.6 0.16 22.5 0 0 0.64
OWLv2* 11.23 0.32 31.18 14.97 0.46 32.34 10.84 0.36 30.1 7.36 0.23 31.99 19.35 0.44 43.98 27.04 0.5 54.07 3.92 0.14 27.98 8.05 0.31 25.98 10.59 0.32 33.1 10.15 0.38 26.7 0.04 0.01 5.57
OWLv2 8.18 0.23 32.55 8.5 0.31 27.79 7.21 0.25 28.84 5.64 0.18 31.35 14.18 0.32 44.32 13.04 0.28 46.58 3.62 0.1 36.23 7.22 0.25 28.88 10.86 0.32 33.93 11.7 0.35 33.43 -0.14 -0.01 14.15
LLMDet-L 6.73 0.17 28.19 1.69 0.08 19.97 2.56 0.1 25.59 2.39 0.08 29.92 0.98 0.06 16.26 20.82 0.37 56.26 27.37 0.46 59.5 2.17 0.13 16.68 5.37 0.19 28.26 3.73 0.16 23.32 0.24 0.04 6.1
Gemini 2.5 9.67 0.19 45.51 5.83 0.19 30.66 5.61 0.14 40.07 0.38 0.01 38.14 10.92 0.24 45.52 18.28 0.26 70.29 26.57 0.36 73.81 8.18 0.2 40.91 9.48 0.22 43.1 8.66 0.23 37.65 2.8 0.08 34.99
SAM3 49.57 0.76 65.17 46.61 0.78 60.13 45.58 0.76 60.35 38.64 0.62 62.56 52.96 0.79 67.21 70.07 0.89 78.73 65.8 0.82 80.67 38.06 0.66 57.62 44.36 0.67 66.05 42.07 0.72 58.36 51.53 0.86 59.98

Annotation format

The annotation format is derived from COCO format. Notable data fields are:

  • images: a list of dict features, contains a list of all image-NP pairs. Each entry is related to an image-NP pair and has the following items.

    • id: an int feature, unique identifier for the image-NP pair
    • text_input: a string feature, the noun phrase for the image-NP pair
    • file_name: a string feature, the relative image path in the corresponding data folder.
    • height/width: dimension of the image
    • is_instance_exhaustive: Boolean (0 or 1). If it's 1 then all the instances are correctly annotated. For instance segmentation, we only use those datapoints. Otherwise, there may be either missing instances or crowd segments (a segment covering multiple instances)
    • is_pixel_exhaustive: Boolean (0 or 1). If it's 1, then the union of all masks cover all pixels corresponding to the prompt. This is weaker than instance_exhaustive since it allows crowd segments. It can be used for semantic segmentation evaluations.

  • annotations: a list of dict features, containing a list of all annotations including bounding box, segmentation mask, area etc.

    • image_id: an int feature, maps to the identifier for the image-np pair in images
    • bbox: a list of float features, containing bounding box in [x,y,w,h] format, normalized by the image dimensions
    • segmentation: a dict feature, containing segmentation mask in RLE format
    • category_id: For compatibility with the coco format. Will always be 1 and is unused.
    • is_crowd: Boolean (0 or 1). If 1, then the segment overlaps several instances (used in cases where instances are not separable, for e.g. due to poor image quality)

  • categories: a list of dict features, containing a list of all categories. Here, we provide the category key for compatibility with the COCO format, but in open-vocabulary detection we do not use it. Instead, the text prompt is stored directly in each image (text_input in images). Note that in our setting, a unique image (id in images) actually corresponds to an (image, text prompt) combination.

For id in images that have corresponding annotations (i.e. exist as image_id in annotations), we refer to them as a "positive" NP. And, for id in images that don't have any annotations (i.e. they do not exist as image_id in annotations), we refer to them as a "negative" NP.

A sample annotation from DROID domain looks as follows:

images 

[
  {
    "id": 10000000,
    "file_name": "AUTOLab_failure_2023-07-07_Fri_Jul__7_18:50:36_2023_recordings_MP4_22008760/00002.jpg",
    "text_input": "the large wooden table",
    "width": 1280,
    "height": 720,
    "queried_category": "3",
    "is_instance_exhaustive": 1,
    "is_pixel_exhaustive": 1
  }
]

annotations 

[
  {
    "area": 0.17324327256944444,
    "id": 1,
    "image_id": 10000000,
    "source": "created by SAM3",
    "bbox": [
      0.03750000149011612,
      0.5083333253860474,
      0.8382812738418579,
      0.49166667461395264
    ],
    "segmentation": {
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      "size": [
        720,
        1280
      ]
    },
    "category_id": 1,
    "iscrowd": 0
  }
]

Data Stats

Here are the stats for the 10 annotation domains. The # Image-NPs represent the total number of unique image-NP pairs including both “positive” and “negative” NPs.

Domain # Image-NPs # Image-NP-Masks
BDD100k 5546 13210
DROID 9445 11098
Ego4D 12608 24049
MyFoodRepo-273 20985 28347
GeoDE 14850 7570
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