Welcome to Detectron 2

About Detectron 2

Detectron2, developed by Facebook AI Research (FAIR), is a cutting-edge open-source software library designed to empower researchers and developers in the field of computer vision. Built on the robust PyTorch framework, Detectron2 specializes in the creation of sophisticated computer vision models, with a primary focus on object detection and instance segmentation tasks. This library offers an impressive array of features, including a modular and flexible architecture, making customization and extension of detection pipelines seamless for a wide range of applications. Detectron2 boasts an extensive collection of state-of-the-art models, capable of accurate and efficient training and inference across various GPU configurations. The library's versatility is enhanced by its rich toolset, encompassing data preprocessing, visualization, evaluation, and debugging utilities. Furthermore, the presence of a dynamic community ensures ample support, resources, and insights for newcomers and seasoned practitioners alike. With its model zoo providing pre-trained weights and a user-friendly approach to custom model training, Detectron2 has become an essential asset for advancing computer vision research and applications, extending its utility beyond object detection and instance segmentation to encompass diverse tasks in the realm of visual perception.

Potential Applications of Detectron 2

• Modularity and Flexibility: Detectron2 is designed with a modular and flexible architecture that allows users to easily customize and extend various components of the detection pipeline. This makes it suitable for both research and production-level projects.
• State-of-the-Art Models: Detectron2 includes implementations of a wide range of state-of-the-art object detection and instance segmentation models. These models have been pre-trained on large datasets and can be fine-tuned on your specific data.
• Efficient Training and Inference: Detectron2 is optimized for training and inference efficiency, making it suitable for large-scale experiments and real-time applications. It supports multi-GPU and distributed training, as well as efficient data loading.
• Rich Set of Tools: The library provides tools for data preprocessing, visualization, evaluation, and debugging. It also offers pre-processing pipelines for common data formats, like COCO and Pascal VOC.
• Community Support: Detectron2 has gained popularity and has a growing community of researchers and practitioners. This means you can find tutorials, examples, and discussions online to help you get started and solve issues.
• Model Zoo: Detectron2 comes with a model zoo that provides pre-trained weights for various object detection and instance segmentation models. This allows you to easily start with a well-performing baseline model and fine-tune it on your specific dataset.
• Wide Range of Applications: Detectron2 is not limited to object detection and instance segmentation. It can be used for various computer vision tasks, including keypoint detection, panoptic segmentation, and more.

Detectron 2 is a powerful new tool with the potential to change the way we interact with computers. It is still under development, but it is already being used by researchers and developers to build new and innovative applications. In the years to come, Detectron 2 is likely to play an increasingly important role in our lives. Detectron2 is a popular library for object detection and image segmentation tasks. Below is a basic example of how you can use Detectron2 to perform object detection on an image.

Research Papers

• Paper 1 : Original paper by Developers
• Paper 2: Introduction and basics about Detectron 2

Videos

• Video 1: Introduction to Detectron 2
• Video 2: Example of image detection using Detectron 2

        
# -- coding: utf-8 --
"""Basic Code of Detectron 2 -Lokesh Kumar

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1Y9NUqu-Do99h-5ZqzzUU56lMSmhwtafJ

## Installing Necessary Libraries
"""

!pip install torch torchvision torchaudio -f https://download.pytorch.org/whl/cpu/torch_stable.html
!pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/index.html


from google.colab import drive
drive.mount('/content/drive')

!ls /content/drive/MyDrive/'Colab Notebooks'

import cv2
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog
from detectron2.utils.visualizer import Visualizer
from detectron2.engine import DefaultPredictor
from detectron2 import model_zoo

# Load image
image_path = "path_to_your_image.jpg"
image = cv2.imread(image_path)

# Initialize Detectron2 model
cfg = get_cfg()
cfg.MODEL.DEVICE = "cpu"  # Use "cuda" for GPU inference
cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"))
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5  # Set threshold for object detection
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml")
predictor = DefaultPredictor(cfg)

# Perform object detection
outputs = predictor(image)

# Get class labels and boxes
instances = outputs["instances"]
class_ids = instances.pred_classes.cpu().numpy()
scores = instances.scores.cpu().numpy()
boxes = instances.pred_boxes.tensor.cpu().numpy()

# Visualize detections on the image
metadata = MetadataCatalog.get(cfg.DATASETS.TRAIN[0])
v = Visualizer(image[:, :, ::-1], metadata=metadata, scale=1.0)
out = v.draw_instance_predictions(instances.to("cpu"))
output_image = out.get_image()[:, :, ::-1]

# Display the result
cv2.imshow("Object Detection", output_image)
cv2.waitKey(0)
cv2.destroyAllWindows()