dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# proj 2 > 2023-10-08 11:56am
+https://universe.roboflow.com/yolo-po0ro/proj-2-qmdk0
+
+Provided by a Roboflow user
+License: CC BY 4.0
+

README.md

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+---
+task_categories:
+- image-classification
+tags:
+- roboflow
+- roboflow2huggingface
+
+---
+
+<div align="center">
+  <img width="640" alt="sargishunanyan/thermo-classification" src="https://huggingface.co/datasets/sargishunanyan/thermo-classification/resolve/main/thumbnail.jpg">
+</div>
+
+### Dataset Labels
+
+```
+['Thermostat', 'Housing', 'Insert']
+```
+
+
+### Number of Images
+
+```json
+{'valid': 102, 'test': 52, 'train': 372}
+```
+
+
+### 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("sargishunanyan/thermo-classification", name="full")
+example = ds['train'][0]
+```
+
+### Roboflow Dataset Page
+[https://universe.roboflow.com/yolo-po0ro/proj-2-qmdk0/dataset/3](https://universe.roboflow.com/yolo-po0ro/proj-2-qmdk0/dataset/3?ref=roboflow2huggingface)
+
+### Citation
+
+```
+@misc{ proj-2-qmdk0_dataset,
+    title = { proj 2 Dataset },
+    type = { Open Source Dataset },
+    author = { Yolo },
+    howpublished = { \\url{ https://universe.roboflow.com/yolo-po0ro/proj-2-qmdk0 } },
+    url = { https://universe.roboflow.com/yolo-po0ro/proj-2-qmdk0 },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2023 },
+    month = { oct },
+    note = { visited on 2023-10-18 },
+}
+```
+
+### License
+CC BY 4.0
+
+### Dataset Summary
+This dataset was exported via roboflow.com on October 8, 2023 at 7:58 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 and search unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+For state of the art Computer Vision training notebooks you can use with this dataset,
+visit https://github.com/roboflow/notebooks
+
+To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
+
+The dataset includes 526 images.
+Car-parts are annotated in folder format.
+
+The following pre-processing was applied to each image:
+
+No image augmentation techniques were applied.
+
+
+

README.roboflow.txt

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+
+proj 2 - v3 2023-10-08 11:56am
+==============================
+
+This dataset was exported via roboflow.com on October 8, 2023 at 7:58 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 and search unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+For state of the art Computer Vision training notebooks you can use with this dataset,
+visit https://github.com/roboflow/notebooks
+
+To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
+
+The dataset includes 526 images.
+Car-parts are annotated in folder format.
+
+The following pre-processing was applied to each image:
+
+No image augmentation techniques were applied.
+
+

data/test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:93f09a5c5f0fd2890a26af38acc03e4700ee2536ad6968d4a1c7276217e6c87e
+size 1687631

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d7d440ba332458112863e2bacfa94c75dd9301636ae260936f84e908c19e3689
+size 10292860

data/valid-mini.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:50fc3d3075db190931c8658a1e26cf911e6fb0b22ac44c2c21da1a7f8997d077
+size 337298

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ae7ed7379d7e881aaef40cac53980f7ecaaf69fdb0a2eee6513542b70704f184
+size 3004481

split_name_to_num_samples.json

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+{"valid": 102, "test": 52, "train": 372}

thermo-classification.py

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+import os
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+_HOMEPAGE = "https://universe.roboflow.com/yolo-po0ro/proj-2-qmdk0/dataset/3"
+_LICENSE = "CC BY 4.0"
+_CITATION = """\
+@misc{ proj-2-qmdk0_dataset,
+    title = { proj 2 Dataset },
+    type = { Open Source Dataset },
+    author = { Yolo },
+    howpublished = { \\url{ https://universe.roboflow.com/yolo-po0ro/proj-2-qmdk0 } },
+    url = { https://universe.roboflow.com/yolo-po0ro/proj-2-qmdk0 },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2023 },
+    month = { oct },
+    note = { visited on 2023-10-18 },
+}
+"""
+_CATEGORIES = ['Thermostat', 'Housing', 'Insert']
+
+
+class THERMOCLASSIFICATIONConfig(datasets.BuilderConfig):
+    """Builder Config for thermo-classification"""
+
+    def __init__(self, data_urls, **kwargs):
+        """
+        BuilderConfig for thermo-classification.
+
+        Args:
+          data_urls: `dict`, name to url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(THERMOCLASSIFICATIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_urls = data_urls
+
+
+class THERMOCLASSIFICATION(datasets.GeneratorBasedBuilder):
+    """thermo-classification image classification dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        THERMOCLASSIFICATIONConfig(
+            name="full",
+            description="Full version of thermo-classification dataset.",
+            data_urls={
+    "train": "https://huggingface.co/datasets/sargishunanyan/thermo-classification/resolve/main/data/train.zip",
+    "validation": "https://huggingface.co/datasets/sargishunanyan/thermo-classification/resolve/main/data/valid.zip",
+    "test": "https://huggingface.co/datasets/sargishunanyan/thermo-classification/resolve/main/data/test.zip",
+}
+,
+        ),
+        THERMOCLASSIFICATIONConfig(
+            name="mini",
+            description="Mini version of thermo-classification dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/sargishunanyan/thermo-classification/resolve/main/data/valid-mini.zip",
+                "validation": "https://huggingface.co/datasets/sargishunanyan/thermo-classification/resolve/main/data/valid-mini.zip",
+                "test": "https://huggingface.co/datasets/sargishunanyan/thermo-classification/resolve/main/data/valid-mini.zip",
+            },
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "image_file_path": datasets.Value("string"),
+                    "image": datasets.Image(),
+                    "labels": datasets.features.ClassLabel(names=_CATEGORIES),
+                }
+            ),
+            supervised_keys=("image", "labels"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+            task_templates=[ImageClassification(image_column="image", label_column="labels")],
+        )
+
+    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={
+                    "files": dl_manager.iter_files([data_files["train"]]),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "files": dl_manager.iter_files([data_files["validation"]]),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "files": dl_manager.iter_files([data_files["test"]]),
+                },
+            ),
+]
+
+    def _generate_examples(self, files):
+        for i, path in enumerate(files):
+            file_name = os.path.basename(path)
+            if file_name.endswith((".jpg", ".png", ".jpeg", ".bmp", ".tif", ".tiff")):
+                yield i, {
+                    "image_file_path": path,
+                    "image": path,
+                    "labels": os.path.basename(os.path.dirname(path)),
+                }