peerannot

A Python library for managing and learning from crowdsourced labels in image classification tasks—

The peerannot library was created to handle crowdsourced labels in classification problems.

Install

To install peerannot, simply run

pip install peerannot

Otherwise, a setup.cfg file is located at the root directory. Installing the library gives access to the Command Line Interface using the keyword peerannot in a bash terminal. Try it out using:

peerannot --help

Quick start

Our library comes with files to download and install standard datasets from the crowdsourcing community. Those are located in the datasets folder

peerannot install ./datasets/cifar10H/cifar10h.py

Running aggregation strategies

In python, we can run classical aggregation strategies from the current dataset as follows

for strat in ["MV", "NaiveSoft", "DS", "GLAD", "WDS"]:
    ! peerannot aggregate . -s {strat}

This will create a new folder names labels containing the labels in the labels_cifar10H_${strat}.npy file.

Training your network

Once the labels are available, we can train a neural network with PyTorch as follows. In a terminal:

for strat in ["MV", "NaiveSoft", "DS", "GLAD", "WDS"]:
    ! peerannot train . -o cifar10H_${strat} \
                -K 10 \
                --labels=./labels/labels_cifar-10h_${strat}.npy \
                --model resnet18 \
                --img-size=32 \
                --n-epochs=1000 \
                --lr=0.1 --scheduler -m 100 -m 250 \
                --num-workers=8

End-to-end strategies

Finally, for the end-to-end strategies using deep learning (as CoNAL or CrowdLayer), the command line is:

peerannot aggregate-deep . -o cifar10h_crowdlayer \
                     --answers ./answers.json \
                     --model resnet18 -K=10 \
                     --n-epochs 150 --lr 0.1 --optimizer sgd \
                     --batch-size 64 --num-workers 8 \
                     --img-size=32 \
                     -s crowdlayer

For CoNAL, the hyperparameter scaling can be provided as -s CoNAL[scale=1e-4].

Peerannot and the crowdsourcing formatting

In peerannot, one of our goals is to make crowdsourced datasets under the same format so that it is easy to switch from one learning or aggregation strategy without having to code once again the algorithms for each dataset.

So, what is a crowdsourced dataset? We define each dataset as:

dataset
├── train
│     ├── class1
│     │     ├─ task0-<key>.png
│     │     ├─ task1-<anotherkey>.png
│     │     ├─ ...
│     │     └─ taskn0-<anotherotherkey>.png
│     ├── class2
│     ├── ...
│     └── classK
├── val
├── test
├── dataset.py
├── metadata.json
└── answers.json

The crowdsourced labels for each training task are contained in the anwers.json file. They are formatted as follows:

{
    0: {<worker_id>: <label>, <another_worker_id>: <label>},
    1: {<yet_another_worker_id>: <label>,}
}

Note that the task index in the answers.json file might not match the order of tasks in the train folder… Thence, each task’s name contains the associated votes file index. The number of tasks in the train folder must match the number of entry keys in the answers.json file.

The metadata.json file contains general information about the dataset. A minimal example would be:

{
    "name": <dataset>,
    "n_classes": K,
    "n_workers": <n_workers>,
}

Create you own dataset

The dataset.py is not mandatory but is here to facilitate the dataset’s installation procedure. A minimal example:

class mydataset:
    def __init__(self):
        self.DIR = Path(__file__).parent.resolve()
        # download the data needed
        # ...

    def setfolders(self):
        print(f"Loading data folders at {self.DIR}")
        train_path = self.DIR / "train"
        test_path = self.DIR / "test"
        valid_path = self.DIR / "val"

        # Create train/val/test tasks with matching index
        # ...

        print("Created:")
        for set, path in zip(
            ("train", "val", "test"), [train_path, valid_path, test_path]
        ):
            print(f"- {set}: {path}")
        self.get_crowd_labels()
        print(f"Train crowd labels are in {self.DIR / 'answers.json'}")

    def get_crowd_labels(self):
        # create answers.json dictionnary in presented format
        # ...
        with open(self.DIR / "answers.json", "w") as answ:
            json.dump(dictionnary, answ, ensure_ascii=False, indent=3)

Release files for peerannot 0.0.1.post33

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for peerannot 0.0.1.post33
File	Size	Uploaded
peerannot-0.0.1.post33.tar.gz	40.7 kB	Nov 13, 2023	Details

Built distribution (wheel)

Table of built distributions (wheels) for peerannot 0.0.1.post33
File	Interpreter	ABI	Platform	Reset
peerannot-0.0.1.post33-py3-none-any.whl 58.7 kB Nov 13, 2023	Python 3	none	any	Details

Total release size: 99.4 kB

Release files / peerannot-0.0.1.post33.tar.gz

Download URL	peerannot-0.0.1.post33.tar.gz
Size	40.7 kB
Tags	Source
SHA-256 checksum How to use checksums	`26ef04809c3da2ca4853be39defccd28d91984d762640f4d8cf37f4ad28d5436`
BLAKE2b-256 checksum How to use checksums	`8dfb5eb12b2cc1fefc9706bd4569baa40da54c92874bdc76bbfa6da3124d602e`
Upload date	Nov 13, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.9.18`

Release files / peerannot-0.0.1.post33-py3-none-any.whl

Download URL	peerannot-0.0.1.post33-py3-none-any.whl
Size	58.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`525cc8c4750ef7d9b84ab8d62a4cc86746975e88a833c7daefa959351e2eb487`
BLAKE2b-256 checksum How to use checksums	`cdfc21f0047460323063d968698f0c36934ef69e1c0c134bec034941f9fdc687`
Upload date	Nov 13, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.9.18`