Stars
Liquid Audio - Speech-to-Speech audio models by Liquid AI
Learn to fine-tune a Small Language Model and embed it into an iOS application
Build and publish crates with pyo3, cffi and uniffi bindings as well as rust binaries as python packages
ACL 2025: Synthetic data generation pipelines for text-rich images.
Repository hosting code for "Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations" (https://arxiv.org/abs/2402.17152).
Estimate dataset difficulty and detect label mistakes using reconstruction error ratios!
Open source AI/ML capabilities for the FiftyOne ecosystem
[NeurIPS2023] DatasetDM:Synthesizing Data with Perception Annotations Using Diffusion Models
Run SOTA Vision-Language Model Florence-2 on your data!
Code for the paper Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models @ CVPR 2024
Code to scrape CVPR website for list of accepted papers, find their arXiv links, extract metadata, and download pdfs
Hugging Face Plugins for FiftyOne
code for CVPR2024 paper: DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction
FiftyOne Plugin for Stable Diffusion Data Augmentation
Official implementation of "Describing Differences in Image Sets with Natural Language" (CVPR 2024 Oral)
🤩 An AWESOME Curated List of Papers, Workshops, Datasets, and Challenges from CVPR 2024
A repository for the FiftyOne Plugin Outlier Detection
Official code for "FeatUp: A Model-Agnostic Frameworkfor Features at Any Resolution" ICLR 2024
Track model training experiments with MLflow and FiftyOne!
TripoSR: Fast 3D Object Reconstruction from a Single Image
A repo that shows a demo of a mlflow and fiftyone integration
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
Testbed for multimodal retrieval augmented generation techniques with FiftyOne, LlamaIndex, and Milvus
This is an Audio Loader Plugin for FiftyOne.
Caption images across your datasets with state of the art models from Hugging Face and Replicate!
PyTorch implementation of CLIP Maximum Mean Discrepancy (CMMD) for evaluating image generation models.