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PyTorch
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Deep learning framework.
- Categories
- Matched as
- pytorch
- torch
Momentum
Each signal is ranked as a percentile among the technologies that have it. A signal without enough history is not counted, and never shown as zero. How momentum works
Momentum
Collecting0 of 5 signalsNo score yet. Momentum is published only when at least 2 of the 5 signals have history. PyTorch has 0, so there is no single number: each signal is shown on its own below. It is not a zero, and nothing is estimated in its place.
Snapshot OCT 24, 2026 · Scoring tech@1
- Official repository stars
- Collecting
- Star growth needs repository snapshots 30 days apart.103,616 stars now
- Member repository stars
- Collecting
- Star growth needs repository snapshots 30 days apart.0 new members in 30 days
- Package adoption
- Not published by its registries
- Its packages’ registries publish no adoption figure, so there is nothing to grow.
- Open models
- No official models
- No official model on Hugging Face, so this signal does not apply.
- News
- Collecting
- News counts once there are 3 events across the last 60 days; it has 2.2 in the last 30 days · 0 in the 30 before
Adoption by registry
The growth of PyTorch’s own packages, one row per registry in that registry’s own unit. Registries count different things over different windows, so they are never added together.
No registry has two observations of PyTorch’s own packages about 30 days apart yet, so there is no growth to show. Nothing is estimated in its place.
News activity
- Events · 7 days
- 2published events that mention it
- Events · 30 days
- 2published events that mention it
- Previous 30 days
- 0events in the 30 days before
Momentum history
Momentum history starts OCT 8, 2026. A chart appears once two daily snapshots carry a score; none does so far.
Repositories
Official 1
- pytorch/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
103.6K stars
Members 794repositories tagged with one of its aliases, most-starred first
- POSTECH-CVLab/PyTorch-StudioGAN
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.
3.5K stars
- PennyLaneAI/pennylane
PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.
3.5K stars
- open-mmlab/mmyolo
OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc.
3.5K stars
- ShangtongZhang/DeepRL
Modularized Implementation of Deep RL Algorithms in PyTorch
3.5K stars
- XinJingHao/DRL-Pytorch
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
3.4K stars
- aqlaboratory/openfold
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
3.4K stars
- opengeos/geoai
GeoAI: Artificial Intelligence for Geospatial Data
3.4K stars
- clovaai/CRAFT-pytorch
Official implementation of Character Region Awareness for Text Detection (CRAFT)
3.4K stars
- chaofengc/IQA-PyTorch
🔎 🖼️ 🔥PyTorch Toolbox for Image Quality Assessment, including PSNR, SSIM, LPIPS, FID, NIQE, NRQM(Ma), MUSIQ, TOPIQ, NIMA, DBCNN, BRISQUE, PI and more...
3.4K stars
- meetps/pytorch-semseg
Semantic Segmentation Architectures Implemented in PyTorch
3.4K stars
- catalyst-team/catalyst
Accelerated deep learning R&D
3.4K stars
- tianzhi0549/FCOS
FCOS: Fully Convolutional One-Stage Object Detection (ICCV'19)
3.3K stars
Packages
“Is” packages are PyTorch itself; ecosystem packages depend on them. The two are counted separately and never added together. Adoption is each registry’s own figure, with its percentile within that registry.
Is 1the technology itself, most-adopted first
- torch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Not published by PyPI
Ecosystem 582packages that depend on it, most-adopted first
- jkp-depth-anything-v2
Depth Anything V2: Robust Monocular Depth Estimation
Not published by PyPI - julius
Nice DSP sweets: resampling, FFT Convolutions. All with PyTorch, differentiable and with CUDA support.
Not published by PyPI - khoj
Your Second Brain
Not published by PyPI - kittenml
Text-to-speech with voice cloning and expression control, plus ultra-lightweight models that run on CPU
Not published by PyPI - kokoro
TTS
Not published by PyPI - kornia
Open Source Differentiable Computer Vision Library for PyTorch
Not published by PyPI - kronos-model-arch
Kronos: A Foundation Model for the Language of Financial Markets
Not published by PyPI - labml-nn
🧑🏫 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit), optimizers (adam, radam, adabelief), gans(dcgan, cyclegan, stylegan2), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, diffusion, etc. 🧠
Not published by PyPI - lahgtna-omnivoice
OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models
Not published by PyPI - laya
Fast, non-autoregressive System 1 decision engine with calibrated probabilities
Not published by PyPI - lazyllm-magic-pdf
A practical tool for converting PDF to Markdown
Not published by PyPI - learnbyvideo-whisper
Robust Speech Recognition via Large-Scale Weak Supervision
Not published by PyPI
Latest news the 3 latest events that mention it
- 40h agoOCT 24, 2026
- 3d agoOCT 22, 2026
- 4mo agoJUL 10, 2026