Search Results for "tensorflow" - Page 9
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Showing 244 open source projects for "tensorflow"
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NNVM
Open deep learning compiler stack for cpu, gpu
The vision of the Apache NNVM Project is to host a diverse community of experts and practitioners in machine learning, compilers, and systems architecture to build an accessible, extensible, and automated open-source framework that optimizes current and emerging machine learning models for any hardware platform. Compilation of deep learning models into minimum deployable modules. Infrastructure to automatically generates and optimize models on more backend with better performance.... -
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AI learning
AiLearning, data analysis plus machine learning practice
We actively respond to the Research Open Source Initiative (DOCX) . Open source today is not just open source, but datasets, models, tutorials, and experimental records. We are also exploring other categories of open source solutions and protocols. I hope you will understand this initiative, combine this initiative with your own interests, and do what you can. Everyone's tiny contributions, together, are the entire open source ecosystem. We are iBooker, a large open-source community,... -
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Keras.js
Run Keras models in the browser, with GPU support using WebGL
Run Keras models in the browser, with GPU support provided by WebGL 2. Models can be run in Node.js as well, but only in CPU mode. Because Keras abstracts away a number of frameworks as backends, the models can be trained in any backend, including TensorFlow, CNTK, etc. Check out the demos/ directory for real examples running Keras.js in VueJS. Library version compatibility, Keras 2.1.2. -
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Neural Network signal recognition rtlsdr
Deep learning signal classification (recognition) using rtl-sdr dongle
WARNING: Outdated version here. Everything has been moved to github: https://github.com/randaller/cnn-rtlsdr -
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Tensorflow LXD container
A tensorflow enabled LXD container
An Ubuntu 14.04 LXD container with tensorflow already installed and configured in two virtualenv environments: one for Python 2 and the other for Python 3. You just need to import the lxd image and activate the virtualenv of your choice. Import tensorflow container: lxc image import <file> --alias tensorflow-image where <file> is the name of the downloaded container file. Launch the container: lxd start tensorflow-image tensorflow Use the container shell: sudo lxc exec... -
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Awesome Recurrent Neural Networks
A curated list of resources dedicated to RNN
A curated list of resources dedicated to recurrent neural networks (closely related to deep learning). Provides a wide range of works and resources such as a Recurrent Neural Network Tutorial, a Sequence-to-Sequence Model Tutorial, Tutorials by nlintz, Notebook examples by aymericdamien, Scikit Flow (skflow) - Simplified Scikit-learn like Interface for TensorFlow, Keras (Tensorflow / Theano)-based modular deep learning library similar to Torch, char-rnn-tensorflow by sherjilozair, char-rnn... -
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Tensorflow-JS-Examples
Examples on using Tensorflow JS. Man at work.
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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. -
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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. -
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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. -
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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. -
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ArtificialIntelligenceHomeSecurity
Deeplearning based model for home security.. attach a webcam run this
Home Security software designed based on Deep Learning architecture using widely used opensource tensorflow platform. It has been tested on ubuntu 15.04/16.04 OS on AMD64 and ARM architecture. The software is distributed under APACHE licence (see licence). The standalone binary executable requires minimal external dependencies as the libraries such as tensorflow, openCV, pygame and others are linked within the software. The software works by capturing the image using inbuilt/USB powered webcam... -
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What is Python? Python is a powerful and easy-to-learn programming language known for its simplicity and versatility. It is widely used in web development, data science, automation, artificial intelligence, and more. With Python, you can: Build web applications with frameworks like Django and Flask Analyze data and develop AI models using libraries like Pandas and TensorFlow Automate repetitive tasks with simple scripts Create scalable and efficient software solutions https...
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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. -
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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. -
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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. -
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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. -
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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. -
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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... -
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deberta-v3-base
Improved DeBERTa model with ELECTRA-style pretraining
... parameters. DeBERTa-v3-base was trained on 160GB of text data, the same used for DeBERTa-v2, ensuring robust language understanding. It achieves state-of-the-art results on several NLU benchmarks, including SQuAD 2.0 and MNLI, outperforming prior models like RoBERTa-base and ELECTRA-base. The model is compatible with Hugging Face Transformers, PyTorch, TensorFlow, and Rust, and is widely used in text classification and fill-mask tasks. -
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paraphrase-multilingual-mpnet-base-v2
Multilingual sentence embeddings for search and similarity tasks
... the MPNet framework, it offers multilingual support with strong performance across a wide range of languages. It can be used via the sentence-transformers library for streamlined access or directly through Hugging Face Transformers with custom pooling operations. The model is compatible with multiple formats, including PyTorch, TensorFlow, ONNX, and OpenVINO. With over 3 million downloads per month, it’s widely adopted in both research and production environments. -
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vit-base-patch16-224
Transformer model for image classification with patch-based input.
...-quality representations that can be adapted to downstream visual tasks with minimal additional training. This model has 86.6 million parameters and is available in PyTorch, TensorFlow, and JAX implementations. While the model card was written by Hugging Face, the weights were originally converted from JAX to PyTorch by the community. -
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twitter-roberta-base-sentiment-latest
RoBERTa model for English sentiment analysis on Twitter data
.... This updated version improves performance over earlier Twitter sentiment models. It supports both PyTorch and TensorFlow and includes example pipelines for quick implementation. With strong classification accuracy and ease of use, it’s ideal for social media monitoring, brand sentiment tracking, and public opinion research. -
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esm2_t30_150M_UR50D
Protein language model trained for sequence understanding and tasks
... unlabeled protein data. This particular checkpoint uses 30 layers and is available in both PyTorch and TensorFlow, facilitating integration into various protein modeling pipelines. It is designed to help researchers extract meaningful representations from protein sequences and accelerate downstream discoveries in computational biology. -
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electra-base-discriminator
Transformer model trained to detect fake vs real tokens efficiently
... classification, question answering (e.g., SQuAD), and sequence labeling. It can be fine-tuned for various downstream NLP tasks and supports multiple frameworks including PyTorch, TensorFlow, JAX, and Rust. ELECTRA models have demonstrated state-of-the-art performance on several benchmarks while training faster than comparable models.
