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AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
[CVPR 2025 Highlight] Truncated Diffusion Model for Real-Time End-to-End Autonomous Driving
Toolkit for linearizing PDFs for LLM datasets/training
Official PyTorch implementation of CODA-LM(https://arxiv.org/abs/2404.10595)
source code for ICLR'22 paper "VOS: Learning What You Don’t Know by Virtual Outlier Synthesis"
[CVPR 2024] Exploring Orthogonality in Open World Object Detection
OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection
Official repository for our work on micro-budget training of large-scale diffusion models.
Papers related to remote sensing in CVPR 2024
Official PyTorch Implementation of Bucketed Ranking-based Losses for Efficient Training of Object Detectors [ECCV2024]
[ECCV 2024 - Oral Presentation] Python library that provides tools for calibrating object detectors and evaluating them
Implementation of "Hierarchical Novelty Detection for Traffic Sign Recognition"
A simple method to perform semi-supervised learning with limited data.
Generating synthetic traffic sign datasets
This program uses a deep neural network with several convolutional layers to classify traffic signs. The model is able to recognize traffic signs with an accuracy of 96,2%. It was trained and valid…
ICLR2024 Spotlight: curation/training code, metadata, distribution and pre-trained models for MetaCLIP; CVPR 2024: MoDE: CLIP Data Experts via Clustering
[ICCV 2023] A latent space for stochastic diffusion models
Augmenting existing datasets of traffic signs by using a Generative Adversarial Network to create synthetic images that will increase the accuracy and generalization ability of classification models.
Traffic sign sources and sprites for mapillary.com
Object detection, 3D detection, and pose estimation using center point detection:
Sample scripts for the Bosch Small Traffic Lights Dataset
Repository for "How to Boost Face Recognition with StyleGAN?"
Codes for RoadBEV: road surface reconstruction in Bird's Eye View
Face Renderer to perform Domain (Face) Specific Data Augmentation