Update dataset card and add dataset builder

README.md

@@ -1,3 +1,67 @@
 ---
+language:
+- en
 license: gpl-3.0
+tags:
+- vision
+- image-segmentation
+- instance-segmentation
+- object-detection
+- optical-flow
+- depth
+- synthetic
+- sim-to-real
+annotations_creators:
+- machine-generated
+pretty_name: SMVB Dataset
+size_categories:
+- 1K<n<10K
+task_categories:
+- object-detection
+- zero-shot-object-detection
+- image-segmentation
+- depth-estimation
+- video-classification
+- other
+task_ids:
+- instance-segmentation
+- semantic-segmentation
 ---
+
+# Synthetic Multimodal Video Benchmark (SMVB)
+
+A dataset consisting of synthetic images from distinct synthetic scenes, annotated with object/instance/semantic segmentation masks, depth data, surface normal information and optical for testing and benchmarking model performance for multi-task/multi-objective learning.
+
+### Supported Tasks and Leaderboards
+
+The dataset supports tasks such as semantic segmentation, instance segmentation, object detection, image classification, depth, surface normal, and optical flow estimation, and video object segmentation.
+
+## Dataset Structure
+
+### Data Instances
+
+
+### Data Fields
+
+### Data Splits
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+### Source Data
+
+### Citation Information
+
+```bibtex
+@INPROCEEDINGS{karoly2024synthetic,
+  author={Károly, Artúr I. and Nádas, Imre and Galambos, Péter},
+  booktitle={2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI)}, 
+  title={Synthetic Multimodal Video Benchmark (SMVB): Utilizing Blender for rich dataset generation}, 
+  year={2024},
+  volume={},
+  number={},
+  pages={},
+  doi={}}
+```

SMVB.py

@@ -0,0 +1,140 @@
+ # 
+ # This file is part of the SMVB distribution (https://huggingface.co/datasets/ABC-iRobotics/SMVB).
+ # Copyright (c) 2023 ABC-iRobotics.
+ # 
+ # This program is free software: you can redistribute it and/or modify  
+ # it under the terms of the GNU General Public License as published by  
+ # the Free Software Foundation, version 3.
+ #
+ # This program is distributed in the hope that it will be useful, but 
+ # WITHOUT ANY WARRANTY; without even the implied warranty of 
+ # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU 
+ # General Public License for more details.
+ #
+ # You should have received a copy of the GNU General Public License 
+ # along with this program. If not, see <http://www.gnu.org/licenses/>.
+ #
+"""SMVB dataset"""
+
+import sys
+import pathlib
+if sys.version_info < (3, 9):
+    from typing import Sequence, Generator, Tuple
+else:
+    from collections.abc import Sequence, Generator
+    Tuple = tuple
+
+from typing import Optional, IO
+
+import datasets
+import itertools
+
+
+# ---- Constants ----
+
+_CITATION = """\
+@INPROCEEDINGS{karoly2024synthetic,
+  author={Károly, Artúr I. and Nádas, Imre and Galambos, Péter},
+  booktitle={2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI)}, 
+  title={Synthetic Multimodal Video Benchmark (SMVB): Utilizing Blender for rich dataset generation}, 
+  year={2024},
+  volume={},
+  number={},
+  pages={},
+  doi={}}
+
+"""
+
+_DESCRIPTION = """\
+Amultimodal video benchmark for evaluating models in multi-task learning scenarios.
+"""
+
+_HOMEPAGE = "https://huggingface.co/ABC-iRobotics/SMVB"
+
+_LICENSE = "GNU General Public License v3.0"
+
+_BASE_URL = "https://huggingface.co/datasets/ABC-iRobotics/SMVB/resolve/main/data"
+
+_VERSION = '1.0.0'
+
+
+# ---- SMVB dataset Configs ----
+
+class SMVBDatasetConfig(datasets.BuilderConfig):
+    """BuilderConfig for SMVB dataset."""
+
+    def __init__(self, name: str, data_urls: Sequence[str],  version: Optional[str] = None, **kwargs):
+        super(SMVBDatasetConfig, self).__init__(version=datasets.Version(version), name=name, **kwargs)
+        self._data_urls = data_urls
+
+    @property
+    def features(self):
+        return datasets.Features(
+            {
+                "image": datasets.Image(),
+                "mask": datasets.Image(),
+                "depth": datasets.Sequence(datasets.Value("float32")),
+                "flow": datasets.Sequence(datasets.Value("float32")),
+                "normal": datasets.Sequence(datasets.Value("float32"))
+            }
+        )
+    
+    @property
+    def supervised_keys(self):
+        return ("image", "mask", "depth", "flow", "normal")
+
+
+
+# ---- SMVB dataset Loader ----
+
+class SMVBDataset(datasets.GeneratorBasedBuilder):
+    """SMVB dataset."""
+
+    BUILDER_CONFIG_CLASS = SMVBDatasetConfig
+    BUILDER_CONFIGS = [
+        SMVBDatasetConfig(
+            name = "all",
+            description = "Photorealistic synthetic images",
+            data_urls = [_BASE_URL],
+            version = _VERSION
+            ),
+    ]
+    DEFAULT_WRITER_BATCH_SIZE = 10
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=self.config.features,
+            supervised_keys=self.config.supervised_keys,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            version=self.config.version,
+        )
+
+    def _split_generators(self, dl_manager):
+        local_data_paths = dl_manager.download(self.config._data_urls)
+        archives = itertools.chain.from_iterable([pathlib.Path(path).rglob('*.tar.gz') for path in local_data_paths])
+        local_data_gen = itertools.chain.from_iterable([dl_manager.iter_archive(path) for path in archives])
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data": local_data_gen
+                }
+            )
+        ]
+
+    def _generate_examples(
+        self,
+        data: Generator[Tuple[str,IO], None, None]
+    ):
+        file_infos = []
+        keys = self.config.supervised_keys
+
+        for i, info in enumerate(data):
+            if file_infos and i%len(keys) == 0:
+                yield (i//len(keys))-1, {k:{'path':d[0],'bytes':d[1].read()} for k,d in zip(keys,file_infos)}
+                file_infos = []
+            file_infos.append(info)