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detection-datasets

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Easily load and transform datasets for object detection

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<br>

# Detection datasets

<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>

---

_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/)    |   [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blinjrm/detection-datasets/blob/main/docs/tutorials/2_Transform.ipynb) |

---

</div>

<br>

# Getting started

## 0. Setup

### Requirements

Python 3.8.1+

`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
```

## 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.

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