Stars
ControlNet++: All-in-one ControlNet for image generations and editing!
Diffusion attentive attribution maps for interpreting Stable Diffusion.
Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG.
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
An open source implementation of Microsoft's VALL-E X zero-shot TTS model. Demo is available in https://plachtaa.github.io/vallex/
Oh my tmux! My self-contained, pretty & versatile tmux configuration made with 💛🩷💙🖤❤️🤍
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
Efficient 3D human pose estimation in video using 2D keypoint trajectories
Quicker way to develop FastApi
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
BoxMOT: Pluggable SOTA multi-object tracking modules modules for segmentation, object detection and pose estimation models
pytorch implementation for "Deep Flow-Guided Video Inpainting"(CVPR'19)
[CVPR19/TPAMI23] SiamMask: A Framework for Fast Online Object Tracking and Segmentation
PyTorch Tutorial for Deep Learning Researchers
Transform depth and RGB image pairs into a .ply file and show it
PyEER is a python package for biometric systems performance evaluation. Includes ROC, DET, FNMR, FMR and CMC curves plotting, scores distribution plotting, EER and operating points estimation. It c…
Monocular Depth Prediction
Keras model trained using semi-hard triplet Loss (tensorflow function) on MNIST
Cascaded Pyramid Network for Multi-Person Pose Estimation (CVPR 2018)
Seamless operability between C++11 and Python
Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications
Keras implementation of RetinaNet object detection.
Classification models trained on ImageNet. Keras.
Python implementation of multi scale retinex with color restoration
Keras implementations of Generative Adversarial Networks.
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.