autodistill-transformers 0.1.1

Use object detection models in Hugging Face Transformers to automatically label data to train a fine-tuned model.

Autodistill Transformers Module

This repository contains the code supporting the Transformers models model for use with Autodistill.

Transformers, maintained by Hugging Face, features a range of state of the art models for Natural Language Processing (NLP), computer vision, and more.

This package allows you to write a function that calls a Transformers object detection model and use it to automatically label data. You can use this data to train a fine-tuned model using an architecture supported by Autodistill (i.e. YOLOv8, YOLOv5, or DETR).

Read the full Autodistill documentation.

Installation

To use Transformers with autodistill, you need to install the following dependency:

pip3 install autodistill-transformers

Quickstart

The following example shows how to use the Transformers module to label images using the Owlv2ForObjectDetection model.

You can update the inference() functon to use any object detection model supported in the Transformers library.

import cv2
import torch
from autodistill.detection import CaptionOntology
from autodistill.utils import plot
from transformers import OwlViTForObjectDetection, OwlViTProcessor

from autodistill_transformers import TransformersModel

processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32")


def inference(image, prompts):
    inputs = processor(text=prompts, images=image, return_tensors="pt")
    outputs = model(**inputs)

    target_sizes = torch.Tensor([image.size[::-1]])

    results = processor.post_process_object_detection(
        outputs=outputs, target_sizes=target_sizes, threshold=0.1
    )[0]

    return results


base_model = TransformersModel(
    ontology=CaptionOntology(
        {
            "a photo of a person": "person",
            "a photo of a cat": "cat",
        }
    ),
    callback=inference,
)

# run inference
results = base_model.predict("image.jpg", confidence=0.1)

print(results)

# plot results
plot(
    image=cv2.imread("image.jpg"),
    detections=results,
    classes=base_model.ontology.classes(),
)

# label a directory of images
base_model.label("./context_images", extension=".jpeg")

License

This project is licensed under an MIT license.

🏆 Contributing

We love your input! Please see the core Autodistill contributing guide to get started. Thank you 🙏 to all our contributors!