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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
-- roboflow2huggingface
-- Manufacturing
----
-
-<div align="center">
-  <img width="640" alt="nflechas/recycling_app" src="https://huggingface.co/datasets/nflechas/recycling_app/resolve/main/thumbnail.jpg">
-</div>
-
-### Dataset Labels
-
-```
-['biodegradable', 'cardboard', 'glass', 'metal', 'paper', 'plastic']
-```
-
-
-### Number of Images
-
-```json
-{'valid': 2098, 'test': 1042, 'train': 7324}
-```
-
-
-### How to Use
-
-- Install [datasets](https://pypi.org/project/datasets/):
-
-```bash
-pip install datasets
-```
-
-- Load the dataset:
-
-```python
-from datasets import load_dataset
-
-ds = load_dataset("nflechas/recycling_app", name="full")
-example = ds['train'][0]
-```
-
-### Roboflow Dataset Page
-[https://universe.roboflow.com/material-identification/garbage-classification-3/dataset/2](https://universe.roboflow.com/material-identification/garbage-classification-3/dataset/2?ref=roboflow2huggingface)
-
-### 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 2023-03-31 },
-}
-```
-
-### 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/valid.zip → full/recycling_app-test.parquet

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-size 38312703
+oid sha256:f4fd70b17ef504481a2dbbcedcdf01d3096cb7b9b0d3765568ad8540eb5ce894
+size 19923137

data/train.zip → full/recycling_app-train.parquet

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-size 137708705
+oid sha256:8ed5dfa6ab0ed2f6b435a66613291cb0fa405d6fad7e9546f56a4e36d658cd92
+size 141596144

data/test.zip → full/recycling_app-validation.parquet

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-size 19639153
+oid sha256:ea27667aefdbc47ff8a47501e1afd3fbc7003ef9a1928748007466e25a165baa
+size 38971753

thumbnail.jpg → mini/recycling_app-test.parquet

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-size 87808
+oid sha256:6a79dcc401aea06951f27b5c9d2a314f1d24204d3bed67732dbb1d1275c74ed1
+size 58748

data/valid-mini.zip → mini/recycling_app-train.parquet

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+size 58748

mini/recycling_app-validation.parquet

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recycling_app.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 2023-03-31 },
-}
-"""
-_CATEGORIES = ['biodegradable', 'cardboard', 'glass', 'metal', 'paper', 'plastic']
-_ANNOTATION_FILENAME = "_annotations.coco.json"
-
-
-class RECYCLING_APPConfig(datasets.BuilderConfig):
-    """Builder Config for recycling_app"""
-
-    def __init__(self, data_urls, **kwargs):
-        """
-        BuilderConfig for recycling_app.
-
-        Args:
-          data_urls: `dict`, name to url to download the zip file from.
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(RECYCLING_APPConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
-        self.data_urls = data_urls
-
-
-class RECYCLING_APP(datasets.GeneratorBasedBuilder):
-    """recycling_app object detection dataset"""
-
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        RECYCLING_APPConfig(
-            name="full",
-            description="Full version of recycling_app dataset.",
-            data_urls={
-                "train": "https://huggingface.co/datasets/nflechas/recycling_app/resolve/main/data/train.zip",
-                "validation": "https://huggingface.co/datasets/nflechas/recycling_app/resolve/main/data/valid.zip",
-                "test": "https://huggingface.co/datasets/nflechas/recycling_app/resolve/main/data/test.zip",
-            },
-        ),
-        RECYCLING_APPConfig(
-            name="mini",
-            description="Mini version of recycling_app dataset.",
-            data_urls={
-                "train": "https://huggingface.co/datasets/nflechas/recycling_app/resolve/main/data/valid-mini.zip",
-                "validation": "https://huggingface.co/datasets/nflechas/recycling_app/resolve/main/data/valid-mini.zip",
-                "test": "https://huggingface.co/datasets/nflechas/recycling_app/resolve/main/data/valid-mini.zip",
-            },
-        )
-    ]
-
-    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(self.config.data_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)
-        filename_to_image = {image["file_name"]: image for image in annotations["images"]}
-
-        for filename in os.listdir(folder_dir):
-            filepath = os.path.join(folder_dir, filename)
-            if filename in filename_to_image:
-                image = filename_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

split_name_to_num_samples.json

@@ -1 +0,0 @@
-{"valid": 2098, "test": 1042, "train": 7324}