Added loading script and updated readme

README.md

@@ -1,3 +1,19 @@
 ---
 license: mit
+dataset_info:
+  config_name: suim
+  features:
+  - name: img
+    dtype: image
+  - name: mask
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 511917
+    num_examples: 1525
+  - name: test
+    num_bytes: 35774
+    num_examples: 110
+  download_size: 183261195
+  dataset_size: 547691
 ---

suim.py

@@ -0,0 +1,132 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset
+# script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Semantic Segmentation of Underwater IMagery (SUIM) dataset"""
+
+
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{islam2020suim,
+  title={{Semantic Segmentation of Underwater Imagery: Dataset and Benchmark}},
+  author={Islam, Md Jahidul and Edge, Chelsey and Xiao, Yuyang and Luo, Peigen and Mehtaz, 
+              Muntaqim and Morse, Christopher and Enan, Sadman Sakib and Sattar, Junaed},
+  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
+  year={2020},
+  organization={IEEE/RSJ}
+}
+"""
+
+_DESCRIPTION = """\
+The SUIM dataset is a dataset for semantic segmentation of underwater imagery.
+
+The dataset consists of 1525 annotated images for training/validation and
+110 samples for testing.
+
+| Object category                  | Symbol | RGB color code |
+|----------------------------------|--------|----------------|
+| Background (waterbody)           | BW     | 000 (black)    |
+| Human divers                     | HD     | 001 (blue)     |
+| Aquatic plants and sea-grass     | PF     | 010 (green)    |
+| Wrecks and ruins                 | WR     | 011 (sky)      |
+| Robots (AUVs/ROVs/instruments)   | RO     | 100 (red)      |
+| Reefs and invertebrates          | RI     | 101 (pink)     |
+| Fish and vertebrates             | FV     | 110 (yellow)   |
+| Sea-floor and rocks              | SR     | 111 (white)    |
+
+
+For more information about the original SUIM dataset,
+please visit the official dataset page:
+
+https://irvlab.cs.umn.edu/resources/suim-dataset
+
+Please refer to the original dataset source for any additional details,
+citations, or specific usage guidelines provided by the dataset creators.
+"""
+
+_HOMEPAGE = "https://irvlab.cs.umn.edu/resources/suim-dataset"
+
+_LICENSE = "mit"
+
+
+class ExDark(datasets.GeneratorBasedBuilder):
+    """Semantic Segmentation of Underwater IMagery (SUIM) dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="suim",
+            version=VERSION,
+            description="Semantic Segmentation of Underwater IMagery (SUIM) dataset",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "suim"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "img": datasets.Image(),
+                    "mask": datasets.Image(),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract("SUIM.zip")
+        train_dir = os.path.join(data_dir, "SUIM", "train_val")
+        test_dir = os.path.join(data_dir, "SUIM", "TEST")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": train_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_dir": test_dir,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_dir, split):
+        img_dir = os.path.join(data_dir, "images")
+        masks_dir = os.path.join(data_dir, "masks")
+        img_files = os.listdir(img_dir)
+
+        for idx, img_file in enumerate(img_files):
+            img_path = os.path.join(img_dir, img_file)
+            mask_path = os.path.join(
+                masks_dir,
+                img_file.replace(".jpg", ".bmp"),
+            )
+            record = {
+                "img": img_path,
+                "mask": mask_path,
+            }
+            yield idx, record