#COCO

This PyTorch-based implementation of the Single Shot MultiBox Detector offers a streamlined approach for efficient object detection. Compatible with popular datasets and offering straightforward processes for setup, training, and evaluation, this project supports NVIDIA GPU acceleration and real-time training performance enhancements via Visdom integration. Users can explore transfer learning with pre-trained model weights, supported by comprehensive instructions for both command-line and Jupyter notebook demos. Regular updates aim to expand capabilities, including support for SSD512 and custom dataset training.

Utilizing Masked Image Modeling with a Vanilla ViT, this project enhances object detection and instance segmentation. A compact convolutional stem is integrated for multi-scale representation, forming a hybrid ViT-ConvNet backbone. It achieves significant results on COCO with 51.7 box AP and 46.2 mask AP, showcasing efficiency in training and accuracy in inference through varied sample ratios.

Learn about the QueryInst method, a query-based instance segmentation approach that offers enhanced accuracy and speed through parallel supervision. Understand its effectiveness in object detection, instance, and video segmentation. Access seamless integration with mmdetection and review its performance on the COCO benchmark. Follow guidance on employing this method using available configurations and checkpoints, and explore its application in expansive instance-level recognition tasks.

VLDet is an open-source project focused on aligning object detection tasks with language processing to enhance open-vocabulary capabilities. Leveraging image-text pairs and tackling the bipartite matching problem, VLDet excels in Open-vocabulary LVIS and COCO datasets. Designed for scalability, it integrates easily with new vocabularies and operates on Linux or macOS with Python ≥ 3.7 and PyTorch ≥ 1.9. Built on the Detectron2 framework, VLDet supports fine-tuning and evaluation of pretrained models, making it adaptable for new object categories without bias or exaggeration.

Discover DETR's novel object detection method using Transformers, ensuring efficient and parallel predictions with reduced complexity. Learn through PyTorch examples and explore its application in computer vision.