dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# GARBAGE CLASSIFICATION 3 > GC1
+https://universe.roboflow.com/object-detection/garbage-classification-3
+
+Provided by Roboflow
+License: CC BY 4.0
+
+# Garbage Object-Detection to Identify Disposal Class
+This dataset detects various kinds of waste, labeling with a class that indentifies how it should be disposed

README.md

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+---
+task_categories:
+- object-detection
+tags:
+- roboflow
+---
+
+### Roboflow Dataset Page
+https://universe.roboflow.com/material-identification/garbage-classification-3/dataset/2
+
+### Dataset Labels
+
+```
+['biodegradable', 'cardboard', 'glass', 'metal', 'paper', 'plastic']
+```
+
+### Citation
+
+```
+@misc{ garbage-classification-3_dataset,
+    title = { GARBAGE CLASSIFICATION 3 Dataset },
+    type = { Open Source Dataset },
+    author = { Material Identification },
+    howpublished = { \\url{ https://universe.roboflow.com/material-identification/garbage-classification-3 } },
+    url = { https://universe.roboflow.com/material-identification/garbage-classification-3 },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { mar },
+    note = { visited on 2023-01-01 },
+}
+```
+
+### License
+CC BY 4.0
+
+### Dataset Summary
+This dataset was exported via roboflow.com on July 27, 2022 at 5:44 AM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+It includes 10464 images.
+GARBAGE-GARBAGE-CLASSIFICATION are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Resize to 416x416 (Stretch)
+
+The following augmentation was applied to create 1 versions of each source image:
+* 50% probability of horizontal flip
+* 50% probability of vertical flip
+* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down
+
+
+

README.roboflow.txt

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+
+GARBAGE CLASSIFICATION 3 - v2 GC1
+==============================
+
+This dataset was exported via roboflow.com on July 27, 2022 at 5:44 AM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+It includes 10464 images.
+GARBAGE-GARBAGE-CLASSIFICATION are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Resize to 416x416 (Stretch)
+
+The following augmentation was applied to create 1 versions of each source image:
+* 50% probability of horizontal flip
+* 50% probability of vertical flip
+* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down
+
+

data/test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ab7ff77c40ead366d65e6ee63dc48eeafb94e4a48ca5fa731d35ba0c979792c0
+size 19639153

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d576a642cb0209ea32118b30166e4376a7e461f77fbefdf2332572a941726481
+size 137708705

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:baf6c0effc547f423fef9efa0fc74afb20493ece95c042e8fcfbdd2531763711
+size 38312703

garbage-object-detection.py

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+import collections
+import json
+import os
+
+import datasets
+
+
+_HOMEPAGE = "https://universe.roboflow.com/material-identification/garbage-classification-3/dataset/2"
+_LICENSE = "CC BY 4.0"
+_CITATION = """\
+@misc{ garbage-classification-3_dataset,
+    title = { GARBAGE CLASSIFICATION 3 Dataset },
+    type = { Open Source Dataset },
+    author = { Material Identification },
+    howpublished = { \\url{ https://universe.roboflow.com/material-identification/garbage-classification-3 } },
+    url = { https://universe.roboflow.com/material-identification/garbage-classification-3 },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { mar },
+    note = { visited on 2023-01-01 },
+}
+"""
+_URLS = {
+    "train": "https://huggingface.co/datasets/keremberke/garbage-object-detection/resolve/main/data/train.zip",
+    "validation": "https://huggingface.co/datasets/keremberke/garbage-object-detection/resolve/main/data/valid.zip",
+    "test": "https://huggingface.co/datasets/keremberke/garbage-object-detection/resolve/main/data/test.zip",
+}
+
+_CATEGORIES = ['biodegradable', 'cardboard', 'glass', 'metal', 'paper', 'plastic']
+_ANNOTATION_FILENAME = "_annotations.coco.json"
+
+
+class GARBAGEOBJECTDETECTION(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image_id": datasets.Value("int64"),
+                "image": datasets.Image(),
+                "width": datasets.Value("int32"),
+                "height": datasets.Value("int32"),
+                "objects": datasets.Sequence(
+                    {
+                        "id": datasets.Value("int64"),
+                        "area": datasets.Value("int64"),
+                        "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                        "category": datasets.ClassLabel(names=_CATEGORIES),
+                    }
+                ),
+            }
+        )
+        return datasets.DatasetInfo(
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "folder_dir": data_files["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "folder_dir": data_files["validation"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "folder_dir": data_files["test"],
+                },
+            ),
+]
+
+    def _generate_examples(self, folder_dir):
+        def process_annot(annot, category_id_to_category):
+            return {
+                "id": annot["id"],
+                "area": annot["area"],
+                "bbox": annot["bbox"],
+                "category": category_id_to_category[annot["category_id"]],
+            }
+
+        image_id_to_image = {}
+        idx = 0
+        
+        annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
+        with open(annotation_filepath, "r") as f:
+            annotations = json.load(f)
+        category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
+        image_id_to_annotations = collections.defaultdict(list)
+        for annot in annotations["annotations"]:
+            image_id_to_annotations[annot["image_id"]].append(annot)
+        image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
+
+        for filename in os.listdir(folder_dir):
+            filepath = os.path.join(folder_dir, filename)
+            if filename in image_id_to_image:
+                image = image_id_to_image[filename]
+                objects = [
+                    process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
+                ]
+                with open(filepath, "rb") as f:
+                    image_bytes = f.read()
+                yield idx, {
+                    "image_id": image["id"],
+                    "image": {"path": filepath, "bytes": image_bytes},
+                    "width": image["width"],
+                    "height": image["height"],
+                    "objects": objects,
+                }
+                idx += 1