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[CVPR 2023] Official implementation of the paper "Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation"
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
[ICLR 2023] Official implementation of the paper "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection"
[CVPR2024 Highlight]GLEE: General Object Foundation Model for Images and Videos at Scale
《神经网络与深度学习》 邱锡鹏著 Neural Network and Deep Learning
Deep Learning Book Chinese Translation
[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
Natural Language Processing Tutorial for Deep Learning Researchers
✨✨Latest Advances on Multimodal Large Language Models
(TPAMI 2024) A Survey on Open Vocabulary Learning
Collection of AWESOME vision-language models for vision tasks
Recent Advances in Vision and Language PreTrained Models (VL-PTMs)
Reading list for research topics in multimodal machine learning
awesome grounding: A curated list of research papers in visual grounding
This is the third party implementation of the paper Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.
detrex is a research platform for DETR-based object detection, segmentation, pose estimation and other visual recognition tasks.
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
Tensors and Dynamic neural networks in Python with strong GPU acceleration
OpenMMLab Detection Toolbox and Benchmark
Flops counter for convolutional networks in pytorch framework
The dataset for drone based detection and tracking is released, including both image/video, and annotations.
deep learning for image processing including classification and object-detection etc.
Count the MACs / FLOPs of your PyTorch model.