Datasets:

connorhoehn
/

card_display_v1

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README.md DELETED Viewed

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----
-language:
-- en
-dataset_info:
-- config_name: card-detection
-  features:
-  - name: image_id
-    dtype: int64
-  - name: image
-    dtype: image
-  - name: width
-    dtype: int32
-  - name: height
-    dtype: int32
-  - name: objects
-    list:
-    - name: category_id
-      dtype:
-        class_label:
-          names:
-            0: boxed
-            1: grid
-            2: spread
-            3: stack
-    - name: image_id
-      dtype: string
-    - name: id
-      dtype: int64
-    - name: area
-      dtype: int64
-    - name: bbox
-      sequence: float32
-      length: 4
-    - name: iscrowd
-      dtype: bool
-  splits:
-  - name: train
-  download_size: 96890427
-  dataset_size: 0
-- config_name: display-detection
-  features:
-  - name: image_id
-    dtype: int64
-  - name: image
-    dtype: image
-  - name: width
-    dtype: int32
-  - name: height
-    dtype: int32
-  - name: objects
-    list:
-    - name: category_id
-      dtype:
-        class_label:
-          names:
-            0: boxed
-            1: grid
-            2: spread
-            3: stack
-    - name: image_id
-      dtype: string
-    - name: id
-      dtype: int64
-    - name: area
-      dtype: int64
-    - name: bbox
-      sequence: float32
-      length: 4
-    - name: iscrowd
-      dtype: bool
-  splits:
-  - name: train
-    num_bytes: 42942
-    num_examples: 154
-  download_size: 96967919
-  dataset_size: 42942
----

card_display_v1.py DELETED Viewed

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-# Copyright 2022 Daniel van Strien.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Card Display Detection"""
-import collections
-import json
-import os
-from typing import Any, Dict, List
-import pandas as pd
-import datasets
-_CITATION = """Connor Hoehn"""
-_DESCRIPTION = "This dataset comprises of card display images from the public domain"
-_HOMEPAGE = "https://www.connorhoehn.com"
-_LICENSE = "Public Domain Mark 1.0"
-_DATASET_URL = "https://www.connorhoehn.com/object_detection_dataset_v2.zip"
-_CATEGORIES = ["boxed","grid","spread","stack"]
-class CardDisplayDetectorConfig(datasets.BuilderConfig):
-    """BuilderConfig for card display dataset."""
-    def __init__(self, name, **kwargs):
-        super(CardDisplayDetectorConfig, self).__init__(
-            version=datasets.Version("1.0.0"),
-            name=name,
-            description="Card Display Detector",
-            **kwargs,
-        )
-class CardDisplayDetector(datasets.GeneratorBasedBuilder):
-    """Card Display dataset."""
-    BUILDER_CONFIGS = [
-        CardDisplayDetectorConfig("display-detection"),
-    ]
-    def _info(self):
-        features = datasets.Features(
-            {
-                "image_id": datasets.Value("int64"),
-                "image": datasets.Image(),
-                "width": datasets.Value("int32"),
-                "height": datasets.Value("int32"),
-            }
-        )
-        object_dict = {
-            "category_id": datasets.ClassLabel(names=_CATEGORIES),
-            "image_id": datasets.Value("string"),
-            "id": datasets.Value("int64"),
-            "area": datasets.Value("int64"),
-            "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
-            "iscrowd": datasets.Value("bool"),
-        }
-        features["objects"] = [object_dict]
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-    def _split_generators(self, dl_manager):
-        dataset_zip = dl_manager.download_and_extract(_DATASET_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # COCO -> x.json, images/
-                gen_kwargs={
-                    "annotations_file": os.path.join(dataset_zip, "result.json"),
-                    # Annotator indicated there was a folder named 1 that doesn't exist
-                    "image_dir": os.path.join(dataset_zip),
-                },
-            )
-        ]
-    # Return dictionary of unique image_ids that have multiple nested annotations
-    def _get_image_id_to_annotations_mapping(self, annotations: List[Dict]) -> Dict[int, List[Dict[Any, Any]]]:
-        """
-        A helper function to build a mapping from image ids to annotations.
-        """
-        image_id_to_annotations = collections.defaultdict(list)
-        for annotation in annotations:
-            image_id_to_annotations[annotation["image_id"]].append(annotation)
-        return image_id_to_annotations
-    def _generate_examples(self, annotations_file, image_dir):
-        def _image_info_to_example(image_info, image_dir):
-            # from the annotation file
-            image = image_info["file_name"]
-            return {
-                "image_id": image_info["id"],
-                "image": os.path.join(image_dir, image),
-                "width": image_info["width"],
-                "height": image_info["height"],
-            }
-        with open(annotations_file, encoding="utf8") as annotation_json:
-            annotation_data = json.load(annotation_json)
-            images = annotation_data["images"]
-            annotations = annotation_data["annotations"]
-            # dictionary of image_ids with all related annotations (bbox)
-            image_id_to_annotations = self._get_image_id_to_annotations_mapping(
-                annotations
-            )
-            if self.config.name == "display-detection":
-                # yield image_id, features
-                for image_id, image_info in enumerate(images):
-                    #image_info -> (w,h,id,filename)
-                    image_details = _image_info_to_example(image_info, image_dir)
-                    # Get images unit id
-                    annotations = image_id_to_annotations[image_info["id"]]
-                    objects = []
-                    # Add the annotation information to the image details
-                    for annotation in annotations:
-                        del annotation['segmentation']
-                        del annotation['ignore']
-                        objects.append(annotation)
-                    # nested dictionary
-                    image_details["objects"] = objects
-                    yield (image_id, image_details)

display-detection/card_display_v1-train.parquet ADDED Viewed

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+size 96497353

trainer_example.ipynb DELETED Viewed

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-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5a6c5d60-6e65-4a51-a0cf-81f96d540d05",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "instance_type": "ml.t3.medium",
-  "kernelspec": {
-   "display_name": "Python 3 (PyTorch 1.6 Python 3.6 CPU Optimized)",
-   "language": "python",
-   "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/pytorch-1.6-cpu-py36-ubuntu16.04-v1"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}