Popular GitHub Repositories for Sentence Segmentation

OpenMMLab Detection Toolbox and Benchmark

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

deep learning for image processing including classification and object-detection etc.

CVPR 2025 论文和开源项目合集

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

Image augmentation for machine learning experiments.

Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.

Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.

? Scalable embedding, reasoning, ranking for images and sentences with CLIP

cvpr2024/cvpr2023/cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集，极市团队整理

100+ Chinese Word Vectors 上百种预训练中文词向量

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

? All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows

Unsupervised text tokenizer for Neural Network-based text generation.

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

Retrieval and Retrieval-augmented LLMs