Duplicate from keremberke/forklift-object-detection

Co-authored-by: Kerem <keremberke@users.noreply.huggingface.co>

.gitattributes

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+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
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+*.bmp filter=lfs diff=lfs merge=lfs -text
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README.dataset.txt

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+# undefined > raw-images
+https://public.roboflow.ai/object-detection/undefined
+
+Provided by undefined
+License: CC BY 4.0
+
+## About this Dataset
+This dataset was created by exporting images from [images.cv](https://images.cv/dataset/forklift-image-classification-dataset) and labeling them as an object detection dataset. **The dataset contains 421 raw images (v1 - raw-images) and labeled classes include:**
+* forklift
+* person
+
+![Example annotated image from the dataset from the dataset](https://i.imgur.com/a6hWEG4.png)

README.md

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+---
+task_categories:
+- object-detection
+tags:
+- roboflow
+- roboflow2huggingface
+- Manufacturing
+---
+
+<div align="center">
+  <img width="640" alt="keremberke/forklift-object-detection" src="https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/thumbnail.jpg">
+</div>
+
+### Dataset Labels
+
+```
+['forklift', 'person']
+```
+
+
+### Number of Images
+
+```json
+{'test': 42, 'valid': 84, 'train': 295}
+```
+
+
+### 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("keremberke/forklift-object-detection", name="full")
+example = ds['train'][0]
+```
+
+### Roboflow Dataset Page
+[https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1](https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1?ref=roboflow2huggingface)
+
+### Citation
+
+```
+@misc{ forklift-dsitv_dataset,
+    title = { Forklift Dataset },
+    type = { Open Source Dataset },
+    author = { Mohamed Traore },
+    howpublished = { \\url{ https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv } },
+    url = { https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { mar },
+    note = { visited on 2023-01-15 },
+}
+```
+
+### License
+CC BY 4.0
+
+### Dataset Summary
+This dataset was exported via roboflow.ai on April 3, 2022 at 9:01 PM GMT
+
+It includes 421 images.
+Forklift are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+
+No image augmentation techniques were applied.
+
+
+

README.roboflow.txt

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+
+Forklift - v1 raw-images
+==============================
+
+This dataset was exported via roboflow.ai on April 3, 2022 at 9:01 PM GMT
+
+It includes 421 images.
+Forklift are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+
+No image augmentation techniques were applied.
+
+

data/test.zip

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+size 2771676

data/train.zip

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+size 13533922

data/valid-mini.zip

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data/valid.zip

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+size 3774165

forklift-object-detection.py

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+import collections
+import json
+import os
+
+import datasets
+
+
+_HOMEPAGE = "https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1"
+_LICENSE = "CC BY 4.0"
+_CITATION = """\
+@misc{ forklift-dsitv_dataset,
+    title = { Forklift Dataset },
+    type = { Open Source Dataset },
+    author = { Mohamed Traore },
+    howpublished = { \\url{ https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv } },
+    url = { https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv },
+    journal = { Roboflow Universe },
+    publisher = { Roboflow },
+    year = { 2022 },
+    month = { mar },
+    note = { visited on 2023-01-15 },
+}
+"""
+_CATEGORIES = ['forklift', 'person']
+_ANNOTATION_FILENAME = "_annotations.coco.json"
+
+
+class FORKLIFTOBJECTDETECTIONConfig(datasets.BuilderConfig):
+    """Builder Config for forklift-object-detection"""
+
+    def __init__(self, data_urls, **kwargs):
+        """
+        BuilderConfig for forklift-object-detection.
+
+        Args:
+          data_urls: `dict`, name to url to download the zip file from.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(FORKLIFTOBJECTDETECTIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_urls = data_urls
+
+
+class FORKLIFTOBJECTDETECTION(datasets.GeneratorBasedBuilder):
+    """forklift-object-detection object detection dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        FORKLIFTOBJECTDETECTIONConfig(
+            name="full",
+            description="Full version of forklift-object-detection dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/train.zip",
+                "validation": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/valid.zip",
+                "test": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/test.zip",
+            },
+        ),
+        FORKLIFTOBJECTDETECTIONConfig(
+            name="mini",
+            description="Mini version of forklift-object-detection dataset.",
+            data_urls={
+                "train": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/valid-mini.zip",
+                "validation": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/valid-mini.zip",
+                "test": "https://huggingface.co/datasets/keremberke/forklift-object-detection/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

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+{"test": 42, "valid": 84, "train": 295}