openvino
mmcv
| openvino | mmcv | |
|---|---|---|
| 17 | 4 | |
| 5,962 | 5,611 | |
| 3.8% | 1.2% | |
| 10.0 | 7.7 | |
| about 16 hours ago | 8 days ago | |
| C++ | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
openvino
- FLaNK Stack 05 Feb 2024
- QUIK is a method for quantizing LLM post-training weights to 4 bit precision
- Intel OpenVINO 2023.1.0 released
- Intel OpenVINO 2023.1.0 released, open-source toolkit for optimizing and deploying AI inference
- OpenVINO 2023.1.0 released
- [N] Intel OpenVINO 2023.1.0 released, open-source toolkit for optimizing and deploying AI inference
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Powering Anomaly Detection for Industry 4.0
Anomalib is an open-source deep learning library developed by Intel that makes it easy to benchmark different anomaly detection algorithms on both public and custom datasets, all by simply modifying a config file. As the largest public collection of anomaly detection algorithms and datasets, it has a strong focus on image-based anomaly detection. It’s a comprehensive, end-to-end solution that includes cutting-edge algorithms, relevant evaluation methods, prediction visualizations, hyperparameter optimization, and inference deployment code with Intel’s OpenVINO Toolkit.
mmcv
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMCV: OpenMMLab foundational library for computer vision.
- Mmcv - Openmmlab computer vision foundation
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An elegant and strong PyTorch Trainer
I opened source some works (AAAI 21 SeqNet, ICCV 21 MAED, etc) and earned more than 500 stars. After referring to some popular projects (detectron2, pytorch-image-models, and mmcv), based on my personal development experience, I developed a SIMPLE enough, GENERIC enough, and STRONG enough PyTorch Trainer: core-pytorch-utils, also named CPU. CPU covers most details in the process of training a deep neural network, including:
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Why do practitioners still use regular tensorflow? [D]
Pretty much any custom layer, loss, ops, etc. For some of the most common ones used for objection detection, see here, examples include rotated iou/nms, deformable convolutions, focal loss variants, sync batch norm, etc.
What are some alternatives?
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
pytorch-image-models - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
TensorFlow2.0_Notebooks - Implementation of a series of Neural Network architectures in TensorFow 2.0
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
stable-diffusion - Go to lstein/stable-diffusion for all the best stuff and a stable release. This repository is my testing ground and it's very likely that I've done something that will break it.
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
neural-compressor - SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
mmrotate - OpenMMLab Rotated Object Detection Toolbox and Benchmark
nebuly - The user analytics platform for LLMs
aiqc - End-to-end deep learning on your desktop or server.