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Boat_dataset.py

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+# Source: https://github.com/huggingface/datasets/blob/main/templates/new_dataset_script.py
+
+import csv
+import json
+import os
+
+import datasets
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {Boat dataset},
+author={XXX, Inc.},
+year={2024}
+}
+"""
+
+_DESCRIPTION = """\
+This dataset is designed to solve an object detection task with images of boats.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/cj94/Boat_dataset/resolve/main"
+
+_LICENSE = ""
+
+_URLS = {
+    "classes": f"{_HOMEPAGE}/data/classes.txt",
+    "train": f"{_HOMEPAGE}/data/instances_train2023r.jsonl",
+    "val": f"{_HOMEPAGE}/data/instances_val2023r.jsonl",
+}
+
+class BoatDataset(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.1.0")
+    
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="Boat_dataset", version=VERSION, description="Dataset for detecting boats in aerial images."),
+    ]
+
+    DEFAULT_CONFIG_NAME = "Boat_dataset"  # Provide a default configuration
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                'image_id': datasets.Value('int32'),
+                'image_path': datasets.Value('string'),
+                'width': datasets.Value('int32'),
+                'height': datasets.Value('int32'),
+                'objects': datasets.Features({
+                    'id': datasets.Sequence(datasets.Value('int32')),
+                    'area': datasets.Sequence(datasets.Value('float32')),
+                    'bbox': datasets.Sequence(datasets.Sequence(datasets.Value('float32'), length=4)),  # [x, y, width, height]
+                    'category': datasets.Sequence(datasets.Value('int32'))
+                }),
+            }),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # Download all files and extract them
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        # Load class labels from the classes file
+        with open('classes.txt', 'r') as file:
+            classes = [line.strip() for line in file.readlines()]
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "annotations_file": downloaded_files["train"],
+                    "classes": classes,
+                    "split": "train",
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "annotations_file": downloaded_files["val"],
+                    "classes": classes,
+                    "split": "val",
+                }
+            ),
+        ]
+
+    def _generate_examples(self, annotations_file, classes, split):
+        # Process annotations
+        with open(annotations_file, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                try:
+                    data = json.loads(row.strip())
+                    yield key, {
+                        "image_id": data["image_id"],
+                        "image_path": data["image_path"],
+                        "width": data["width"],
+                        "height": data["height"],
+                        "objects": data["objects"],
+                    }
+                except json.JSONDecodeError:
+                    print(f"Skipping invalid JSON at line {key + 1}: {row}")
+                    continue

README.md

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+---
+viewer: false
+---
+
+# Boat Dataset for Object Detection
+
+## Overview
+This dataset contains images of real & virtual boats for object detection tasks. It can be used to train and evaluate object detection models.
+
+## Dataset Structure
+
+### Data Instances
+
+A data point comprises an image and its object annotations.
+
+```
+{'image_id': 0,
+ 'image_path': 'images/0720_0937_2023-07-20-09-37-30_0_middle_color000220.jpg',
+ 'width': 640,
+ 'height': 480,
+ 'objects': {'id': [1],
+  'area': [328.0],
+  'bbox': [[153.69000244140625,
+    101.76499938964844,
+    21.924999237060547,
+    14.972999572753906]],
+  'category': [8]}}
+```
+
+### Data Fields
+
+- `image_id`: the image id
+- `width`: the image width
+- `height`: the image height
+- `objects`: a dictionary containing bounding box metadata for the objects present on the image
+  - `id`: the annotation id
+  - `area`: the area of the bounding box
+  - `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
+  - `category`: the object's category, with possible values including 
+    - `BallonBoat` (0)
+    - `BigBoat` (1)
+    - `Boat` (2)
+    - `JetSki` (3)
+    - `Katamaran` (4)
+    - `SailBoat` (5)
+    - `SmallBoat` (6)
+    - `SpeedBoat` (7)
+    - `WAM_V` (8)
+
+  
+### Data Splits
+
+- `Training dataset` (42833)
+    - `Real`
+        - `WAM_V` (2333)
+    - `Virtual`
+        - `BallonBoat` (4500)
+        - `BigBoat` (4500)
+        - `Boat` (4500)
+        - `JetSki` (4500)
+        - `Katamaran` (4500)
+        - `SailBoat` (4500)
+        - `SmallBoat` (4500)
+        - `SpeedBoat` (4500)
+        - `WAM_V` (4500)
+
+- `Val dataset` (5400)
+    - `Real` 
+        - `WAM_V` (900)
+    - `Virtual`
+        - `BallonBoat` (500)
+        - `BigBoat` (500)
+        - `Boat` (500)
+        - `JetSki` (500)
+        - `Katamaran` (500)
+        - `SailBoat` (500)
+        - `SmallBoat` (500)
+        - `SpeedBoat` (500)
+        - `WAM_V` (500)
+
+
+## Usage
+```
+from datasets import load_dataset
+dataset = load_dataset("zhuchi76/Boat_dataset")
+```
+
+## Citation
+If you use this dataset in your research, please cite the following paper: