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detection-datasets-0.3.5
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مشاهده بیشترتوضیحات
Easily load and transform datasets for object detection
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | detection-datasets-0.3.5 |
| نام | detection-datasets |
| نسخه کتابخانه | 0.3.5 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jerome Blin |
| ایمیل نویسنده | blinjrm@gmail.com |
| آدرس صفحه اصلی | https://github.com/blinjrm/detection-dataset |
| آدرس اینترنتی | https://pypi.org/project/detection-datasets/ |
| مجوز | MIT |
<div align="center">
<img src="https://raw.githubusercontent.com/blinjrm/detection-datasets/main/images/dd_logo.png" alt="logo" width="100"/>
<br>
# Detection datasets
<a href="https://www.python.org/"><img alt="Python" src="https://img.shields.io/badge/-Python 3.7-blue?style=flat-square&logo=python&logoColor=white"></a>
<a href="https://black.readthedocs.io/en/stable/"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-black.svg?style=flat-square&labelColor=gray"></a>
<a href="https://github.com/blinjrm/detection-datasets/actions/workflows/ci.yaml"><img src="https://img.shields.io/github/workflow/status/blinjrm/detection-datasets/CI?label=CI&style=flat-square"/></a>
<a href="https://github.com/blinjrm/detection-datasets/actions/workflows/pypi.yaml"><img src="https://img.shields.io/github/workflow/status/blinjrm/detection-datasets/Python%20package?label=Build&style=flat-square"/></a>
<a href="https://pypi.org/project/detection-datasets/"><img src="https://img.shields.io/pypi/status/detection-datasets?style=flat-square"/></a>
<br>
*Easily load and transform datasets for object detection.*
</div>
<br>
---
**Documentation**: https://blinjrm.github.io/detection-datasets/
**Source Code**: https://github.com/blinjrm/detection-datasets
**Datasets on Hugging Face Hub**: https://huggingface.co/detection-datasets
---
<br>
`detection_datasets` aims to make it easier to work with detection datasets.
This library works alongside the [Detection dataset](https://huggingface.co/detection-datasets) organisation on the 🤗 Hub, where some detection datasets have been uploaded in the format expected by the library, and are ready to use.
The main features are:
* **Read** the dataset :
* From disk if it has already been downloaded.
* Directly from the Hugging Face Hub if it [already exist](https://huggingface.co/detection-datasets).
* **Transform** the dataset:
* Select a subset of data.
* Remap categories.
* Create new train-val-test splits.
* **Visualize** the annotations and images.
* **Write** the dataset:
* To disk, selecting the target detection format: `COCO`, `YOLO` and more to come.
* To the Hugging Face Hub for easy reuse in a different environment and share with the community.
<br>
<div align="center">
---
*Read the quick start bellow, or directly jump to the tutorials:*
| Goal | Tutorial | Colab |
|--------------------------------------|:--------:|:-----:|
| Load from disk and upload to the Hub | [Open in the docs](https://blinjrm.github.io/detection-datasets/tutorials/1_Read/) | [](https://colab.research.google.com/github/blinjrm/detection-datasets/blob/main/docs/tutorials/1_Read.ipynb) |
| Load from the Hub and transform | [Open in the docs](https://blinjrm.github.io/detection-datasets/tutorials/2_Transform/) | [](https://colab.research.google.com/github/blinjrm/detection-datasets/blob/main/docs/tutorials/2_Transform.ipynb) |
---
</div>
<br>
# Getting started
## 0. Setup
### Requirements
Python 3.7+
`detection_datasets` is upon the great work of:
- [Pandas](https://pandas.pydata.org) for manipulating data.
- [Hugging Face Datasets](https://huggingface.co/docs/datasets/index) to store and load datasets from the Hub.
### Installation
```console
$ pip install detection_datasets
```
### Import
```Python
from detection_datasets import DetectionDataset
```
## 1. Read
### From local filesystem
```Python
config = {
'dataset_format': 'coco', # the format of the dataset on disk
'path': 'path/do/data/on/disk', # where the dataset is located
'splits': { # how to read the files
'train': ('train.json', 'train'), # name of the split (annotation file, images directory)
'test': ('test.json', 'test'),
},
}
dd = DetectionDataset()
dd.from_disk(**config)
# note that you can use method cascading as well:
# dd = DetectionDataset().from_disk(**config)
```
### From the Hugging Face Hub
The `detection_dataset` library works alongside the [Detection dataset](https://huggingface.co/detection-datasets) organisation on the Hugging Face Hub, where some detection datasets have been uploaded in the format expected by the library, and are ready to use.
```Python
dd = DetectionDataset().from_hub(name='fashionpedia')
```
Currently supported format for reading datasets are:
- COCO
- *more to come*
The list of datasets available from the Hub is given by:
```Python
# Search in the "detection-datasets" repository on the Hub.
DetectionDataset().available_in_hub()
# Search in another repository on the Hub.
DetectionDataset().available_in_hub(repo_name=MY_REPO_OR_ORGANISATION)
```
## 2. Transform
The supported transformations are:
```Python
# Select a subset of images, perserving the splits and their proportions
dd.select(n_images=1000)
# Shuffle the dataset, perserving the splits and their proportions
dd.shuffle(seed=42)
# Create new train-val-test splits, overwritting the splits from the original dataset
dd.split(splits=[0.8, 0.1, 0.1])
# Map existing categories to new categories.
# The annotations with a category absent from the mapping are dropped.
dd.map_categories(mapping={'existing_category': 'new_category'})
```
These transformations can be chained; for example here we select a subset of 10.000 images and create new train-val-test splits:
```Python
dd = DetectionDataset()\
.from_hub(name='fashionpedia')\
.select(n_images=10000)\
.split(splits=[0.8, 0.1, 0.1])
```
## 3. Visualize
The `DetectionDataset` objects contains several properties to analyze your data:
```Python
dd.data # This is equivlent to calling `dd.get_data('image')`,
# and returns a DataFrame with 1 row per image
dd.get_data('bbox') # Returns a DataFrame with 1 row per annotation
dd.n_images # Number of images
dd.n_bbox # Number of annotations
dd.splits # List of split names
dd.split_proportions # DataFrame with the % of iamges in each split
dd.categories # DataFrame with the categories and thei ids
dd.category_names # List of categories
dd.n_categories # Number of categories
```
You can also visualize a image with its annotations in a notebook:
```Python
dd.show() # Shows a random image from the dataset
dd.show(image_id=42) # Shows the select image based on image_id
```
<div align="center">
<img src="https://raw.githubusercontent.com/blinjrm/detection-datasets/main/images/show.png" alt="image with annotations" width="500"/>
</div>
## 4. Write
### To local filesystem
Once the dataset is ready, you can write it to the local filesystem in a given format:
```Python
dd.to_disk(
dataset_format='yolo',
name='MY_DATASET_NAME',
path='DIRECTORY_TO_WRITE_TO',
)
```
Currently supported format for writing datasets are:
- YOLO
- COCO
- MMDET
- *more to come*
### To the Hugging Face Hub
The dataset can also be easily uploaded to the Hugging Face Hub, for reuse later on or in a different environment:
```Python
dd.to_hub(
dataset_name='MY_DATASET_NAME',
repo_name='MY_REPO_OR_ORGANISATION'
)
```
The dataset viewer on the Hub will work out of the box, and we encourage you to update the README in your new repo to make it easier for the comminuty to use the dataset.
<div align="center">
<img src="https://raw.githubusercontent.com/blinjrm/detection-datasets/main/images/hub.png" alt="hub viewer" width="800"/>
</div>
نیازمندی
| مقدار | نام |
|---|---|
| >=1.3.0,<2.0.0 | pandas |
| >=2.4.0,<3.0.0 | datasets[vision] |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7.1,<4.0.0 | Python |
نحوه نصب
نصب پکیج whl detection-datasets-0.3.5:
pip install detection-datasets-0.3.5.whl
نصب پکیج tar.gz detection-datasets-0.3.5:
pip install detection-datasets-0.3.5.tar.gz