This repository contains the official Pytorch implementation of “Searching for Efficient Multi-Stage Vision Transformers” and is based on DeiT and timm. Illustration of the proposed multi-stage ViT-Res network. Illustration of weight-sharing neural architecture search with multi-architectural sampling. Accuracy-MACs trade-offs of the proposed ViT-ResNAS. Our networks achieves comparable results to previous work. Requirements The codebase is tested with 8 V100 (16GB) GPUs. To install requirements: pip install -r requirements.txt Docker files are provided to set up the environment. Please run: cd […]

MMNas MMNas: Deep Multimodal Neural Architecture Search This repository corresponds to the PyTorch implementation of the MMnas for visual question answering (VQA), visual grounding (VGD), and image-text matching (ITM) tasks. Prerequisites Software and Hardware Requirements You may need a machine with at least 4 GPU (>= 8GB), 50GB memory for VQA and VGD and 150GB for ITM and 50GB free disk space. We strongly recommend to use a SSD drive to guarantee high-speed I/O. You should first install some necessary […]

Auto-Research A no-code utility to generate a detailed well-cited survey with topic clustered sections (draft paper format) and other interesting artifacts from a single research query. Data Provider: arXiv Open Archive Initiative OAI Requires: python 3.7 or above poppler-utils list of requirements in requirements.txt 8GB disk space 13GB CUDA(GPU) memory – for a survey of 100 searched papers(max_search) and 25 selected papers(num_papers) Demo : Video Demo : https://drive.google.com/file/d/1-77J2L10lsW-bFDOGdTaPzSr_utY743g/view?usp=sharing Kaggle Re-usable Demo : https://www.kaggle.com/sidharthpal/auto-research-generate-survey-from-query ([TIP] click ‘edit and run’ to run […]

pyctcdecode A fast and feature-rich CTC beam search decoder for speech recognition written in Python, providing n-gram (kenlm) language model support similar to PaddlePaddle’s decoder, but incorporating many new features such as byte pair encoding and real-time decoding to support models like Nvidia’s Conformer-CTC or Facebook’s Wav2Vec2. pip install pyctcdecode Main Features: 🔥 hotword boosting 🤖 handling of BPE vocabulary 👥 multi-LM support for 2+ models 🕒 stateful LM for real-time decoding ✨ native frame index annotation of words 💨 fast runtime, comparable to C++ implementation […]

alfred-fzf Instant Fuzzy File Search for Alfred List all the files inside a folder using fd, and instantly fuzzy-search through all of them using fzf, all from inside Alfred with a single keyword: fzf. Screenshots Invoke with fzf Search through tens of thousands of files Fuzzy search shows most relevant results There’s a folder action too! Search even inside apps Manually create folder-specific keywords Alternative and comparison Fuzzy Folders is another fuzzy search workflow that is moreconfigurable and customizable. Instant […]

Isearch (OSINT) isearch is an OSINT tool on Instagram. Offers a face recognition reverse image search on Instagram profile feed photos. Disclaimer: **FOR EDUCATIONAL PURPOSE ONLY! ** You might encounter (false positive / false negative) results. This because Face recognition uses hog as a model which is fast but low on accuracy, the other model can be ‘cnn’ which is high on accuracy but very slow (on CPU && fast on GPU) Installation Dlib installation: (full guide: https://www.pyimagesearch.com/2017/03/27/how-to-install-dlib/) # Install […]

NAS-Bench-Macro This repository includes the benchmark and code for NAS-Bench-Macro in paper “Prioritized Architecture Sampling with Monto-Carlo Tree Search”, CVPR2021. NAS-Bench-Macro is a NAS benchmark on macro search space. The NAS-Bench-Macro consists of 6561 networks and their test accuracies, parameters, and FLOPs on CIFAR-10 dataset. Each architecture in NAS-Bench-Macro is trained from scratch isolatedly. Benchmark All the evaluated architectures are stored in file nas-bench-macro_cifar10.json with the following format: { arch1: { test_acc: [float, float, float], // the test accuracies of […]

Deep Image Search Deep Image Search is an AI-based image search engine that includes deep transfer learning features Extraction and tree-based vectorized search technique. Features Faster Search O(logN) Complexity. High Accurate Output Result. Best for Implementing on python based web application or APIs. Best implementation for College students and freshers for project creation. Applications are Images based E-commerce recommendation, Social media and other image-based platforms that want to implement image recommendation and search. Installation This library is compatible with both […]