#other #anomaly_detection #anomaly_localization #anomaly_segmentation #benchmark #computer_vision #dataset #deep_learning #defect_detection #industrial_image

https://github.com/M-3LAB/awesome-industrial-anomaly-detection

#cplusplus #artificial_intelligence #computer_vision #document #document_analysis #document_intelligence #document_recognition #document_understanding #documentai #end_to_end_ocr #multimodal #multimodal_deep_learning #ocr #scene_text_detection #scene_text_detection_recognition #scene_text_recognition #text_detection #text_recognition #vision_language #vision_language_model #vision_language_transformer

https://github.com/AlibabaResearch/AdvancedLiterateMachinery

#python #computer_vision #datasets #deep_learning #earth_observation #geospatial #models #pytorch #remote_sensing #satellite_imagery #torchvision #transforms

https://github.com/microsoft/torchgeo

#python #computer_vision #contrastive_learning #contributions_welcome #deep_learning #embeddings #hacktoberfest #machine_learning #pytorch #self_supervised_learning

https://github.com/lightly-ai/lightly

#python #classification #coco #computer_vision #deep_learning #hacktoberfest #image_processing #instance_segmentation #low_code #machine_learning #metrics #object_detection #oriented_bounding_box #pascal_voc #python #pytorch #tensorflow #tracking #video_processing #yolo

Supervision is a powerful tool for building computer vision applications. It allows you to easily load datasets, draw detections on images or videos, and count detections in specific zones. You can use any classification, detection, or segmentation model with it, and it has connectors for popular libraries like Ultralytics and Transformers. Supervision also offers customizable annotators to visualize your data and utilities to manage datasets in various formats. By using Supervision, you can streamline your computer vision projects and make them more reliable and efficient. Additionally, there are extensive tutorials and documentation available to help you get started quickly.

https://github.com/roboflow/supervision

#python #book #chinese #computer_vision #deep_learning #machine_learning #natural_language_processing #notebook #python

This resource, "Dive into Deep Learning," is a free online book that helps you learn deep learning by doing. It provides detailed concepts, background knowledge, and executable code to help you understand the mathematical principles and implement them in practice. The book includes runnable code examples so you can see how to solve problems step-by-step and experiment with different approaches. It also allows for community feedback and continuous updates to keep up with the rapidly evolving field of deep learning. This makes it an excellent resource for anyone looking to become a deep learning practitioner, whether you're a student or an industry professional.

https://github.com/d2l-ai/d2l-zh

#javascript #annotation #annotation_tool #annotations #boundingbox #computer_vision #data_labeling #dataset #datasets #deep_learning #image_annotation #image_classification #image_labeling #image_labelling_tool #label_studio #labeling #labeling_tool #mlops #semantic_segmentation #text_annotation #yolo

Label Studio is a free, open-source tool that helps you label different types of data like images, audio, text, videos, and more. It has a simple and user-friendly interface that makes it easy to prepare or improve your data for machine learning models. You can customize it to fit your needs and export labeled data in various formats. It supports multi-user labeling, multiple projects, and integration with machine learning models for pre-labeling and active learning. You can install it locally using Docker, pip, or other methods, or deploy it in cloud services like Heroku or Google Cloud Platform. This tool streamlines your data labeling process and helps you create more accurate ML models.

https://github.com/HumanSignal/label-studio

#cplusplus #arknights #computer_vision #maa

MAA Assistant Arknights is a powerful tool designed to help players of the game "Arknights" automate daily tasks. It uses image recognition technology to complete tasks such as daily missions, recruiting operators, and managing base facilities. The tool supports multiple platforms including Windows, Linux, and macOS.

Using MAA Assistant Arknights, you can automatically complete daily routines like collecting credits, shopping, and receiving rewards. It also helps in identifying operator lists, tracking materials needed for development, and optimizing base scheduling. The tool integrates with various platforms like Penguin Logistics and Yituliu to upload data and plan strategies.

By using this assistant, you save time and effort by automating repetitive tasks, allowing you to focus on other aspects of the game or your daily life. Additionally, it supports multiple languages and has an active community for support and development contributions.

Overall, MAA Assistant Arknights makes playing Arknights more efficient and enjoyable by handling mundane tasks automatically.

https://github.com/MaaAssistantArknights/MaaAssistantArknights

#python #chinese #clip #computer_vision #contrastive_loss #coreml_models #deep_learning #image_text_retrieval #multi_modal #multi_modal_learning #nlp #pretrained_models #pytorch #transformers #vision_and_language_pre_training #vision_language

This project is about a Chinese version of the CLIP (Contrastive Language-Image Pretraining) model, trained on a large dataset of Chinese text and images. Here’s what you need to know This model helps you quickly perform tasks like calculating text and image features, cross-modal retrieval (finding images based on text or vice versa), and zero-shot image classification (classifying images without any labeled examples).
- **Ease of Use** The model has been tested on various datasets and shows strong performance in zero-shot image classification and cross-modal retrieval tasks.
- **Resources**: The project includes pre-trained models, training and testing codes, and detailed tutorials on how to use the model for different tasks.

Overall, this project makes it easy to work with Chinese text and images using advanced AI techniques, saving you time and effort.

https://github.com/OFA-Sys/Chinese-CLIP

#jupyter_notebook #computer_vision #deep_learning #drug_discovery #forecasting #large_language_models #mxnet #nlp #paddlepaddle #pytorch #recommender_systems #speech_recognition #speech_synthesis #tensorflow #tensorflow2 #translation

This repository provides top-quality deep learning examples that are easy to train and deploy on NVIDIA GPUs. It includes a wide range of models for computer vision, natural language processing, recommender systems, speech to text, and more. These examples are updated monthly and come in Docker containers with the latest NVIDIA software, ensuring the best performance. The models support multiple GPUs and nodes, and some are optimized for Tensor Cores, which can significantly speed up training. This makes it easier for users to achieve high accuracy and performance in their deep learning projects.

https://github.com/NVIDIA/DeepLearningExamples