dataset uploaded by roboflow2huggingface package

README.dataset.txt

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+# undefined > 640x640 M-H-L
+https://public.roboflow.ai/object-detection/undefined
+
+Provided by undefined
+License: CC BY 4.0
+
+# Smoke Detection Dataset
+
+This computer vision smoke detection dataset contains images of synthsized smoke in both indoor and outdoor settings. Check out the source link below for more information on this dataset.
+
+source: 
+
+Smoke100k dataset
+https://bigmms.github.io/cheng_gcce19_smoke100k/
+
+
+## Use Cases
+- Identifying smoke indoors 
+- Identifying smoke outdoors (but **not** with aerial imagery) 
+- Identifying smoke-like object (eg: mist/steam from humidifiers)
+
+
+
+## Testing
+
+You can test this model by using the [Roboflow Inference Widget](https://blog.roboflow.com/testing-a-computer-vision-model-in-10-seconds-or-less/) found above. The action hits the model inference API, which in turn produces the color coded bounding boxes on the objects the model was trained to detect, along with its labels, and confidence for each prediction. The feature also produces the JSON output provided by the API.
+

README.md

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+---
+task_categories:
+- object-detection
+tags:
+- roboflow
+---
+
+### Roboflow Dataset Page
+https://universe.roboflow.com/smoke-detection/smoke100-uwe4t/dataset/4
+
+### Dataset Labels
+
+```
+['smoke']
+```
+
+### Citation
+
+```
+@misc{ smoke100-uwe4t_dataset,
+    title = { Smoke100 Dataset },
+    type = { Open Source Dataset },
+    author = { Smoke Detection },
+    howpublished = { \\url{ https://universe.roboflow.com/smoke-detection/smoke100-uwe4t } },
+    url = { https://universe.roboflow.com/smoke-detection/smoke100-uwe4t },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { dec },
+    note = { visited on 2023-01-02 },
+}
+```
+
+### License
+CC BY 4.0
+
+### Dataset Summary
+This dataset was exported via roboflow.ai on March 17, 2022 at 3:42 PM GMT
+
+It includes 21578 images.
+Smoke 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 640x640 (Stretch)
+
+No image augmentation techniques were applied.
+
+
+

README.roboflow.txt

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+
+Smoke100 - v4 640x640 M-H-L
+==============================
+
+This dataset was exported via roboflow.ai on March 17, 2022 at 3:42 PM GMT
+
+It includes 21578 images.
+Smoke 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 640x640 (Stretch)
+
+No image augmentation techniques were applied.
+
+

data/test.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f28d7b41394ffdc05a22bbc01e5062aa3308388740840e0bf82a735d746d6ffc
+size 85342865

data/train.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6eba0b1b3fe397ff2f8e30713c1c63da1431f5bad2a60a6b12e78c4fc3469074
+size 603290033

data/valid.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:71d3e20b89b605b8572158af4e7cad5c26b473560e4453c6470ec37aab3d112d
+size 172611095

smoke-object-detection.py

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+import collections
+import json
+import os
+
+import datasets
+
+
+_HOMEPAGE = "https://universe.roboflow.com/smoke-detection/smoke100-uwe4t/dataset/4"
+_LICENSE = "CC BY 4.0"
+_CITATION = """\
+@misc{ smoke100-uwe4t_dataset,
+    title = { Smoke100 Dataset },
+    type = { Open Source Dataset },
+    author = { Smoke Detection },
+    howpublished = { \\url{ https://universe.roboflow.com/smoke-detection/smoke100-uwe4t } },
+    url = { https://universe.roboflow.com/smoke-detection/smoke100-uwe4t },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { dec },
+    note = { visited on 2023-01-02 },
+}
+"""
+_URLS = {
+    "train": "https://huggingface.co/datasets/keremberke/smoke-object-detection/resolve/main/data/train.zip",
+    "validation": "https://huggingface.co/datasets/keremberke/smoke-object-detection/resolve/main/data/valid.zip",
+    "test": "https://huggingface.co/datasets/keremberke/smoke-object-detection/resolve/main/data/test.zip",
+}
+
+_CATEGORIES = ['smoke']
+_ANNOTATION_FILENAME = "_annotations.coco.json"
+
+
+class SMOKEOBJECTDETECTION(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