Datasets:
Tasks:
Object Detection
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keremberke
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dataset uploaded by roboflow2huggingface package
Browse files- README.dataset.txt +12 -0
- README.md +49 -0
- README.roboflow.txt +15 -0
- data/test.zip +3 -0
- data/train.zip +3 -0
- data/valid.zip +3 -0
- forklift-object-detection.py +121 -0
README.dataset.txt
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# undefined > raw-images
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https://public.roboflow.ai/object-detection/undefined
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Provided by undefined
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License: CC BY 4.0
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## About this Dataset
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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:**
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* forklift
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* person
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![Example annotated image from the dataset from the dataset](https://i.imgur.com/a6hWEG4.png)
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README.md
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---
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task_categories:
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- object-detection
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tags:
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- roboflow
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---
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### Roboflow Dataset Page
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https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1
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### Dataset Labels
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```
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['forklift', 'person']
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```
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### Citation
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```
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@misc{ forklift-dsitv_dataset,
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title = { Forklift Dataset },
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type = { Open Source Dataset },
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author = { Mohamed Traore },
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howpublished = { \\url{ https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv } },
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url = { https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv },
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journal = { Roboflow Universe },
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publisher = { Roboflow },
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year = { 2022 },
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month = { mar },
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note = { visited on 2023-01-01 },
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}
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```
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### License
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CC BY 4.0
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### Dataset Summary
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This dataset was exported via roboflow.ai on April 3, 2022 at 9:01 PM GMT
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It includes 421 images.
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Forklift are annotated in COCO format.
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The following pre-processing was applied to each image:
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* Auto-orientation of pixel data (with EXIF-orientation stripping)
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No image augmentation techniques were applied.
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README.roboflow.txt
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Forklift - v1 raw-images
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==============================
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This dataset was exported via roboflow.ai on April 3, 2022 at 9:01 PM GMT
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It includes 421 images.
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Forklift are annotated in COCO format.
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The following pre-processing was applied to each image:
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* Auto-orientation of pixel data (with EXIF-orientation stripping)
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No image augmentation techniques were applied.
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data/test.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:f1a52eeef7da48be78556a1dd9424460c51cb8737176b31c01b56bf92b1c7395
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size 2771676
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data/train.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:d59c69ecf95a6520485444a2a61c349f24618ffa0b54ffee5f9422e08ac0f30d
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size 13533922
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data/valid.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:881c60bacab0a2eed1add7e9dfa12eeb31a3afcbeca4dc15096e35e3ac1e4a86
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size 3774165
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forklift-object-detection.py
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import collections
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import json
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import os
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import datasets
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_HOMEPAGE = "https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1"
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_LICENSE = "CC BY 4.0"
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_CITATION = """\
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@misc{ forklift-dsitv_dataset,
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title = { Forklift Dataset },
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type = { Open Source Dataset },
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author = { Mohamed Traore },
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howpublished = { \\url{ https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv } },
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url = { https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv },
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journal = { Roboflow Universe },
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publisher = { Roboflow },
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year = { 2022 },
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month = { mar },
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note = { visited on 2023-01-01 },
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}
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"""
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_URLS = {
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"train": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/train.zip",
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"validation": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/valid.zip",
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"test": "https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/data/test.zip",
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}
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_CATEGORIES = ['forklift', 'person']
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_ANNOTATION_FILENAME = "_annotations.coco.json"
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class FORKLIFTOBJECTDETECTION(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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def _info(self):
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features = datasets.Features(
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{
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"image_id": datasets.Value("int64"),
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"image": datasets.Image(),
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"width": datasets.Value("int32"),
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"height": datasets.Value("int32"),
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"objects": datasets.Sequence(
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{
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"id": datasets.Value("int64"),
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"area": datasets.Value("int64"),
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"bbox": datasets.Sequence(datasets.Value("float32"), length=4),
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"category": datasets.ClassLabel(names=_CATEGORIES),
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}
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),
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}
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)
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return datasets.DatasetInfo(
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features=features,
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homepage=_HOMEPAGE,
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citation=_CITATION,
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license=_LICENSE,
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)
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def _split_generators(self, dl_manager):
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data_files = dl_manager.download_and_extract(_URLS)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"folder_dir": data_files["train"],
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"folder_dir": data_files["validation"],
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"folder_dir": data_files["test"],
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},
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),
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]
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def _generate_examples(self, folder_dir):
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def process_annot(annot, category_id_to_category):
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return {
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"id": annot["id"],
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"area": annot["area"],
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"bbox": annot["bbox"],
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"category": category_id_to_category[annot["category_id"]],
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}
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image_id_to_image = {}
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idx = 0
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annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
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with open(annotation_filepath, "r") as f:
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annotations = json.load(f)
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category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
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image_id_to_annotations = collections.defaultdict(list)
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for annot in annotations["annotations"]:
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image_id_to_annotations[annot["image_id"]].append(annot)
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image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
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for filename in os.listdir(folder_dir):
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filepath = os.path.join(folder_dir, filename)
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if filename in image_id_to_image:
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image = image_id_to_image[filename]
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objects = [
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process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
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]
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with open(filepath, "rb") as f:
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image_bytes = f.read()
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yield idx, {
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"image_id": image["id"],
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"image": {"path": filepath, "bytes": image_bytes},
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"width": image["width"],
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"height": image["height"],
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"objects": objects,
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}
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idx += 1
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