Search Results for "tensorflow"
Sort By:
48 programs for "tensorflow" with 1 filter applied:
-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
Gen AI apps are built with MongoDB AtlasMongoDB Atlas provides built-in vector search and a flexible document model so developers can build, scale, and run gen AI apps without stitching together multiple databases. From LLM integration to semantic search, Atlas simplifies your AI architecture—and it’s free to get started.
-
1
Frigate
NVR with realtime local object detection for IP cameras
Frigate - NVR With Realtime Object Detection for IP Cameras A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Use of a Google Coral Accelerator is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead. -
2
MLflow
Open source platform for the machine learning lifecycle
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). -
3
frugally-deep
A lightweight header-only library for using Keras (TensorFlow) models
Use Keras models in C++ with ease. A lightweight header-only library for using Keras (TensorFlow) models in C++. Works out-of-the-box also when compiled into a 32-bit executable. (Of course, 64 bit is fine too.) Avoids temporarily allocating (potentially large chunks of) additional RAM during convolutions (by not materializing the im2col input matrix). Utterly ignores even the most powerful GPU in your system and uses only one CPU core per prediction. Quite fast on one CPU core, and you can run... -
4
NVIDIA Merlin
Library providing end-to-end GPU-accelerated recommender systems
... on the NVIDIA developer website. Transform data (ETL) for preprocessing and engineering features. Accelerate your existing training pipelines in TensorFlow, PyTorch, or FastAI by leveraging optimized, custom-built data loaders. Scale large deep learning recommender models by distributing large embedding tables that exceed available GPU and CPU memory. Deploy data transformations and trained models to production with only a few lines of code. -
Test your software product anywhere in the worldGlobal App Testing is a managed pool of freelancers used by Google, Meta, Microsoft, and other world-beating software companies.
-
5
NVIDIA FLARE
NVIDIA Federated Learning Application Runtime Environment
NVIDIA Federated Learning Application Runtime Environment NVIDIA FLARE is a domain-agnostic, open-source, extensible SDK that allows researchers and data scientists to adapt existing ML/DL workflows(PyTorch, TensorFlow, Scikit-learn, XGBoost etc.) to a federated paradigm. It enables platform developers to build a secure, privacy-preserving offering for a distributed multi-party collaboration. NVIDIA FLARE is built on a componentized architecture that allows you to take federated learning... -
6
Transformers4Rec
Transformers4Rec is a flexible and efficient library
Transformers4Rec is an advanced recommendation system library that leverages Transformer models for sequential and session-based recommendations. The library works as a bridge between natural language processing (NLP) and recommender systems (RecSys) by integrating with one of the most popular NLP frameworks, Hugging Face Transformers (HF). Transformers4Rec makes state-of-the-art transformer architectures available for RecSys researchers and industry practitioners. Traditional recommendation... -
7
OPENRNDR
Kotlin library for creative coding, real-time and interactive graphics
... the data coming from these models in order to create compelling (interactive) experiences. The ORML library includes both models and interface code to make the use of those models simple. ORML is built on top of orx-tensorflow which is an OPENRNDR extra that provides tools to wrap and convert between Tensorflow and OPENRNDR primitives. With these integrations, Machine Learning has become more accessible for interactive designers, coders, and developers. -
8
KotlinDL
High-level Deep Learning Framework written in Kotlin
KotlinDL is a high-level Deep Learning API written in Kotlin and inspired by Keras. Under the hood, it uses TensorFlow Java API and ONNX Runtime API for Java. KotlinDL offers simple APIs for training deep learning models from scratch, importing existing Keras and ONNX models for inference, and leveraging transfer learning for tailoring existing pre-trained models to your tasks. This project aims to make Deep Learning easier for JVM and Android developers and simplify deploying deep learning... -
9
Blazeface
Blazeface is a lightweight model that detects faces in images
Blazeface is a lightweight, high-performance face detection model designed for mobile and embedded devices, developed by TensorFlow. It is optimized for real-time face detection tasks and runs efficiently on mobile CPUs, ensuring minimal latency and power consumption. Blazeface is based on a fast architecture and uses deep learning techniques to detect faces with high accuracy, even in challenging conditions. It supports multiple face detection in varying lighting and poses, and is designed... -
Sales CRM and Pipeline Management Software | PipedrivePipedrive’s simple interface empowers salespeople to streamline workflows and unite sales tasks in one workspace. Unlock instant sales insights with Pipedrive’s visual sales pipeline and fine-tune your strategy with robust reporting features and a personalized AI Sales Assistant.
-
10
MoveNet
A CNN model that predicts human joints from RGB images of a person
The MoveNet model is an efficient, real-time human pose estimation system designed for detecting and tracking keypoints of human bodies. It utilizes deep learning to accurately locate 17 key points across the body, providing precise tracking even with fast movements. Optimized for mobile and embedded devices, MoveNet can be integrated into applications for fitness tracking, augmented reality, and interactive systems. -
11
MobileNetV2
SSD-based object detection model trained on Open Images V4
MobileNetV2 is a highly efficient and lightweight deep learning model designed for mobile and embedded devices. It is based on an inverted residual structure that allows for faster computation and fewer parameters, making it ideal for real-time applications on resource-constrained devices. MobileNetV2 is commonly used for image classification, object detection, and other computer vision tasks, achieving high accuracy while maintaining a small memory footprint. It also supports TensorFlow Lite... -
12
Universal Sentence Encoder
Encoder of greater-than-word length text trained on a variety of data
The Universal Sentence Encoder (USE) is a pre-trained deep learning model designed to encode sentences into fixed-length embeddings for use in various natural language processing (NLP) tasks. It leverages Transformer and Deep Averaging Network (DAN) architectures to generate embeddings that capture the semantic meaning of sentences. The model is designed for tasks like sentiment analysis, semantic textual similarity, and clustering, and provides high-quality sentence representations in a... -
13
Acme
A library of reinforcement learning components and agents
Acme is a framework from DeepMind for building scalable and reproducible reinforcement learning agents. It emphasizes modular components, distributed training, and ease of experimentation. -
14
TensorFlow Course
Simple and ready-to-use tutorials for TensorFlow
This repository houses a highly popular (~16k stars) set of TensorFlow tutorials and example code aimed at beginners and intermediate users. It includes Jupyter notebooks and scripts that cover neural network fundamentals, model training, deployment, and more, with support for Google Colab. -
15
d2l-zh
Chinese-language edition of Dive into Deep Learning
d2l‑zh is the Chinese-language edition of Dive into Deep Learning, an interactive, open‑source deep learning textbook that combines code, math, and explanatory text. It features runnable Jupyter notebooks compatible with multiple frameworks (e.g., PyTorch, MXNet, TensorFlow), comprehensive theoretical analysis, and exercises. Widely adopted in over 70 countries and used by more than 500 universities for teaching deep learning. -
16
distilbert-base-uncased
Distilled version of BERT, optimized for speed and efficiency
... range of downstream NLP tasks like sequence classification, token classification, or question answering. While efficient, it inherits biases present in the original BERT model. DistilBERT is available under the Apache 2.0 license and is compatible with PyTorch, TensorFlow, and JAX. -
17
bert-base-portuguese-cased
BERTimbau: BERT model pretrained for Brazilian Portuguese NLP
... language modeling, sentence embeddings, or fine-tuning on a variety of Portuguese-language NLP tasks. It is compatible with both PyTorch and TensorFlow and available under the MIT license. -
18
bert-base-multilingual-cased
Multilingual BERT model trained on 104 Wikipedia languages
... languages. It supports sequence classification, token classification, question answering, and more. Built with a shared vocabulary of 110,000 tokens, it is compatible with both PyTorch and TensorFlow. -
19
xlm-roberta-base
Multilingual RoBERTa trained on 100 languages for NLP tasks
... and classification tasks, offering strong performance on benchmarks across languages. It supports use in PyTorch, TensorFlow, JAX, and ONNX, and is best utilized when fine-tuned for downstream applications such as sentiment analysis, named entity recognition, or question answering. -
20
clip-vit-base-patch32
Zero-shot image-text matching with ViT-B/32 Transformer encoder
... multiple frameworks including PyTorch, TensorFlow, and JAX. It is primarily intended for research and robustness evaluation in computer vision, not for commercial deployment. Like other CLIP models, it performs well across a wide range of benchmarks but exhibits known limitations in fine-grained classification and demographic bias. Despite strong generalization, OpenAI discourages its use in facial recognition or unconstrained real-world applications without in-domain testing. -
21
blip-image-captioning-base
Image captioning model trained on COCO using BLIP base architecture
... web-sourced noisy image-caption data using synthetic caption generation and noise filtering. BLIP's unified architecture is designed for both vision-language understanding and generation, showing strong generalization even in zero-shot settings. The model can be easily deployed using Hugging Face Transformers in PyTorch or TensorFlow, with support for GPU acceleration and half-precision inference. -
22
bart-large-cnn
Summarization model fine-tuned on CNN/DailyMail articles
... in generating concise, coherent, and human-readable summaries from longer texts. Its architecture allows it to model both language understanding and generation tasks effectively. The model supports usage in PyTorch, TensorFlow, and JAX, and is integrated with the Hugging Face pipeline API for simple deployment. Due to its size and performance, it's widely used in real-world summarization applications such as news aggregation, legal document condensing, and content creation. -
23
vit-base-patch16-224-in21k
Base Vision Transformer pretrained on ImageNet-21k at 224x224
... fine-tuned heads, it provides strong image representations useful for transfer learning and feature extraction. The model is compatible with PyTorch, TensorFlow, and JAX, and includes a pretrained pooler that facilitates downstream use cases. It is typically used by adding a linear classification head on top of the [CLS] token's output. The ViT architecture demonstrated that transformers, when scaled and trained properly, can match or exceed convolutional models in image recognition. -
24
esm2_t36_3B_UR50D
3B parameter ESM-2 model for protein sequence understanding
... acid sequences as input and generates embeddings or masked predictions, enabling fine-tuning for specific biological applications. Larger checkpoints like this one tend to yield better performance but require more compute resources. The model is compatible with PyTorch and TensorFlow, and Meta provides demo notebooks to help with fine-tuning and application. Its capabilities support advanced bioinformatics research and computational biology workflows. -
25
distilbert-base-uncased-finetuned-sst-2
Sentiment analysis model fine-tuned on SST-2 with DistilBERT
distilbert-base-uncased-finetuned-sst-2-english is a lightweight sentiment classification model fine-tuned from DistilBERT on the SST-2 dataset. Developed by Hugging Face, it performs binary sentiment analysis (positive/negative) with high accuracy, achieving 91.3% on the dev set. It offers a smaller and faster alternative to BERT while retaining competitive performance (BERT scores ~92.7%). The model uses an uncased vocabulary and supports PyTorch, TensorFlow, ONNX, and Rust for broad...
