conda create -n SoccerNet python pip
conda activate SoccerNet
pip install SoccerNet
# pip install -e https://github.com/SoccerNet/SoccerNet
# pip install -e .- SoccerNet main folder
- Leagues (england_epl/europe_uefa-champions-league/france_ligue-1/...)
- Seasons (2014-2015/2015-2016/2016-2017)
- Games (format: "{Date} - {Time} - {HomeTeam} {Score} {AwayTeam}")
-
SoccerNet-v2 - Labels / Manual Annotations
- video.ini: information on start/duration for each half of the game in the HQ video, in second
- Labels-v2.json: Labels from SoccerNet-v2 - action spotting
- Labels-cameras.json: Labels from SoccerNet-v1 - camera shot segmentation
-
SoccerNet-v2 - Videos / Automatically Extracted Features
- 1_224p.mkv: 224p video 1st half - timmed with start/duration from HQ video - resolution 224*398 - 25 fps
- 2_224p.mkv: 224p video 2nd half - timmed with start/duration from HQ video - resolution 224*398 - 25 fps
- 1_720p.mkv: 720p video 1st half - timmed with start/duration from HQ video - resolution 720*1280 - 25 fps
- 2_720p.mkv: 720p video 2nd half - timmed with start/duration from HQ video - resolution 720*1280 - 25 fps
- 1_ResNET_TF2.npy: ResNET features @2fps for 1st half from SoccerNet-v2, extracted using TF2
- 2_ResNET_TF2.npy: ResNET features @2fps for 2nd half from SoccerNet-v2, extracted using TF2
- 1_ResNET_TF2_PCA512.npy: ResNET features @2fps for 1st half from SoccerNet-v2, extracted using TF2, with dimensionality reduced to 512 using PCA
- 2_ResNET_TF2_PCA512.npy: ResNET features @2fps for 2nd half from SoccerNet-v2, extracted using TF2, with dimensionality reduced to 512 using PCA
- 1_ResNET_5fps_TF2.npy: ResNET features @5fps for 1st half from SoccerNet-v2, extracted using TF2
- 2_ResNET_5fps_TF2.npy: ResNET features @5fps for 2nd half from SoccerNet-v2, extracted using TF2
- 1_ResNET_5fps_TF2_PCA512.npy: ResNET features @5fps for 1st half from SoccerNet-v2, extracted using TF2, with dimensionality reduced to 512 using PCA
- 2_ResNET_5fps_TF2_PCA512.npy: ResNET features @5fps for 2nd half from SoccerNet-v2, extracted using TF2, with dimensionality reduced to 512 using PCA
- 1_ResNET_25fps_TF2.npy: ResNET features @25fps for 1st half from SoccerNet-v2, extracted using TF2
- 2_ResNET_25fps_TF2.npy: ResNET features @25fps for 2nd half from SoccerNet-v2, extracted using TF2
- 1_player_boundingbox_maskrcnn.json: Player Bounding Boxes @2fps for 1st half, extracted with MaskRCNN
- 2_player_boundingbox_maskrcnn.json: Player Bounding Boxes @2fps for 2nd half, extracted with MaskRCNN
- 1_field_calib_ccbv.json: Field Camera Calibration @2fps for 1st half, extracted with CCBV
- 2_field_calib_ccbv.json: Field Camera Calibration @2fps for 2nd half, extracted with CCBV
- 1_baidu_soccer_embeddings.npy: Frame Embeddings for 1st half from https://github.com/baidu-research/vidpress-sports
- 2_baidu_soccer_embeddings.npy: Frame Embeddings for 2nd half from https://github.com/baidu-research/vidpress-sports
-
Legacy from SoccerNet-v1
- Labels.json: Labels from SoccerNet-v1 - action spotting for goals/cards/subs only
- 1_C3D.npy: C3D features @2fps for 1st half from SoccerNet-v1
- 2_C3D.npy: C3D features @2fps for 2nd half from SoccerNet-v1
- 1_C3D_PCA512.npy: C3D features @2fps for 1st half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
- 2_C3D_PCA512.npy: C3D features @2fps for 2nd half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
- 1_I3D.npy: I3D features @2fps for 1st half from SoccerNet-v1
- 2_I3D.npy: I3D features @2fps for 2nd half from SoccerNet-v1
- 1_I3D_PCA512.npy: I3D features @2fps for 1st half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
- 2_I3D_PCA512.npy: I3D features @2fps for 2nd half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
- 1_ResNET.npy: ResNET features @2fps for 1st half from SoccerNet-v1
- 2_ResNET.npy: ResNET features @2fps for 2nd half from SoccerNet-v1
- 1_ResNET_PCA512.npy: ResNET features @2fps for 1st half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
- 2_ResNET_PCA512.npy: ResNET features @2fps for 2nd half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
-
- Games (format: "{Date} - {Time} - {HomeTeam} {Score} {AwayTeam}")
- Seasons (2014-2015/2015-2016/2016-2017)
- Leagues (england_epl/europe_uefa-champions-league/france_ligue-1/...)
from SoccerNet.Downloader import SoccerNetDownloader
mySoccerNetDownloader = SoccerNetDownloader(LocalDirectory="path/to/soccernet")
# Download SoccerNet labels
mySoccerNetDownloader.downloadGames(files=["Labels.json"], split=["train", "valid", "test"]) # download labels
mySoccerNetDownloader.downloadGames(files=["Labels-v2.json"], split=["train", "valid", "test"]) # download labels SN v2
mySoccerNetDownloader.downloadGames(files=["Labels-cameras.json"], split=["train", "valid", "test"]) # download labels for camera shot
# Download SoccerNet features
mySoccerNetDownloader.downloadGames(files=["1_ResNET_TF2.npy", "2_ResNET_TF2.npy"], split=["train", "valid", "test"]) # download Features
mySoccerNetDownloader.downloadGames(files=["1_ResNET_TF2_PCA512.npy", "2_ResNET_TF2_PCA512.npy"], split=["train", "valid", "test"]) # download Features reduced with PCA
mySoccerNetDownloader.downloadGames(files=["1_player_boundingbox_maskrcnn.json", "2_player_boundingbox_maskrcnn.json"], split=["train", "valid", "test"]) # download Player Bounding Boxes inferred with MaskRCNN
mySoccerNetDownloader.downloadGames(files=["1_field_calib_ccbv.json", "2_field_calib_ccbv.json"], split=["train", "valid", "test"]) # download Field Calibration inferred with CCBV
mySoccerNetDownloader.downloadGames(files=["1_baidu_soccer_embeddings.npy", "2_baidu_soccer_embeddings.npy"], split=["train", "valid", "test"]) # download Frame Embeddings from https://github.com/baidu-research/vidpress-sports
# Download SoccerNet Challenge set (require password from NDA to download videos)
mySoccerNetDownloader.downloadGames(files=["1_ResNET_TF2.npy", "2_ResNET_TF2.npy"], split=["challenge"]) # download ResNET Features
mySoccerNetDownloader.downloadGames(files=["1_ResNET_TF2_PCA512.npy", "2_ResNET_TF2_PCA512.npy"], split=["challenge"]) # download ResNET Features reduced with PCA
mySoccerNetDownloader.downloadGames(files=["1_224p.mkv", "2_224p.mkv"], split=["challenge"]) # download 224p Videos (require password from NDA)
mySoccerNetDownloader.downloadGames(files=["1_720p.mkv", "2_720p.mkv"], split=["challenge"]) # download 720p Videos (require password from NDA)
mySoccerNetDownloader.downloadGames(files=["1_player_boundingbox_maskrcnn.json", "2_player_boundingbox_maskrcnn.json"], split=["challenge"]) # download Player Bounding Boxes inferred with MaskRCNN
mySoccerNetDownloader.downloadGames(files=["1_field_calib_ccbv.json", "2_field_calib_ccbv.json"], split=["challenge"]) # download Field Calibration inferred with CCBV
mySoccerNetDownloader.downloadGames(files=["1_baidu_soccer_embeddings.npy", "2_baidu_soccer_embeddings.npy"], split=["challenge"]) # download Frame Embeddings from https://github.com/baidu-research/vidpress-sports
# Download development kit per task
mySoccerNetDownloader.downloadDataTask(task="calibration-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="caption-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="jersey-2023", split=["train", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="reid-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="spotting-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="spotting-ball-2023", split=["train", "valid", "test", "challenge"], password=<PW_FROM_NDA>)
mySoccerNetDownloader.downloadDataTask(task="tracking-2023", split=["train", "test", "challenge"])
# Download SoccerNet videos (require password from NDA to download videos)
mySoccerNetDownloader.password = "Password for videos? (contact the author)"
mySoccerNetDownloader.downloadGames(files=["1_224p.mkv", "2_224p.mkv"], split=["train", "valid", "test"]) # download 224p Videos
mySoccerNetDownloader.downloadGames(files=["1_720p.mkv", "2_720p.mkv"], split=["train", "valid", "test"]) # download 720p Videos
mySoccerNetDownloader.downloadRAWVideo(dataset="SoccerNet") # download 720p Videos
mySoccerNetDownloader.downloadRAWVideo(dataset="SoccerNet-Tracking") # download single camera RAW Videos
# Download SoccerNet in OSL ActionSpotting format
mySoccerNetDownloader.downloadDataTask(task="spotting-OSL", split=["train", "valid", "test", "challenge"], version="ResNET_PCA512")
mySoccerNetDownloader.downloadDataTask(task="spotting-OSL", split=["train", "valid", "test", "challenge"], version="baidu_soccer_embeddings")
mySoccerNetDownloader.downloadDataTask(task="spotting-OSL", split=["train", "valid", "test", "challenge"], version="224p", password=<PW_FROM_NDA>)from SoccerNet.utils import getListGames
print(getListGames(split="train")) # return list of games recommended for training
print(getListGames(split="valid")) # return list of games recommended for validation
print(getListGames(split="test")) # return list of games recommended for testing
print(getListGames(split="challenge")) # return list of games recommended for challenge
print(getListGames(split=["train", "valid", "test", "challenge"])) # return list of games for training, validation and testing
print(getListGames(split="v1")) # return list of games from SoccerNetv1 (train/valid/test)
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