Search Results for "face" - Page 20
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Showing 503 open source projects for "face"
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Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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A gui based man against machine poker (texas hold them) end user application which will deduce and adapt to the player's strategy.
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FaceIn! is a face recognizing log in application for Linux. You will never have to enter your password anymore! Just show your face to your camera and let the system recognize you and take you to your desktop!
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translategemma-4b-it
Lightweight multimodal translation model for 55 languages
...TranslateGemma uses a structured chat template that enforces explicit source and target language codes, ensuring consistent, deterministic behavior and reducing ambiguity in multilingual pipelines. It integrates seamlessly with Hugging Face Transformers through pipelines or direct model initialization, supporting GPU acceleration and scalable deployment. -
Build Data Resilience - Take the Assessment TodayIs your recovery strategy as strong as you think? Take this quick self-assessment to check your recovery readiness and gain tailored insights. In only 2 minutes, you'll learn where you fall on the recovery readiness scale.
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Qwen2-7B-Instruct
Instruction-tuned 7B language model for chat and complex tasks
...It shows strong performance across benchmarks such as MMLU, MT-Bench, GSM8K, and Humaneval, often surpassing similarly sized open-source models. Designed for conversational use, it integrates with Hugging Face Transformers and supports long-context applications via YARN and vLLM for efficient deployment. -
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Grok-2.5
Large-scale xAI model for local inference with SGLang, Grok-2.5
Grok-2.5 is a large-scale AI model developed and released by xAI in 2024, made available through Hugging Face for research and experimentation. The model is distributed as raw weights that require specialized infrastructure to run, rather than being hosted by inference providers. To use it, users must download over 500 GB of files and set them up locally with the SGLang inference engine. Grok-2.5 supports advanced inference with multi-GPU configurations, requiring at least 8 GPUs with more than 40 GB of memory each for optimal performance. ... -
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bge-large-en-v1.5
BGE-Large v1.5: High-accuracy English embedding model for retrieval
...It is recommended for use in document retrieval tasks, semantic search, and passage reranking, particularly when paired with a reranker like BGE-Reranker. The model supports inference through multiple frameworks, including FlagEmbedding, Sentence-Transformers, LangChain, and Hugging Face Transformers. It accepts English text as input and returns normalized 1024-dimensional embeddings suitable for cosine similarity comparisons. -
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wav2vec2-large-xlsr-53-portuguese
Portuguese ASR model fine-tuned on XLSR-53 for 16kHz audio input
...It achieves a WER of 11.3% (or 9.01% with LM) on Common Voice test data, demonstrating high accuracy for a single-language ASR model. Inference can be done using HuggingSound or via a custom PyTorch script using Hugging Face Transformers and Librosa. Training scripts and evaluation methods are open source and available on GitHub. It is released under the Apache 2.0 license and intended for ASR tasks in Brazilian Portuguese. -
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bge-base-en-v1.5
Efficient English embedding model for semantic search and retrieval
...With 768 embedding dimensions and a maximum sequence length of 512 tokens, it achieves strong performance across multiple MTEB benchmarks, nearly matching larger models while maintaining efficiency. It supports use via SentenceTransformers, Hugging Face Transformers, FlagEmbedding, and ONNX for various deployment scenarios. Typical usage includes normalizing output embeddings and calculating cosine similarity via dot product for ranking. -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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OpenVLA 7B
Vision-language-action model for robot control via images and text
...OpenVLA is MIT-licensed, fully open-source, and designed collaboratively by Stanford, Berkeley, Google DeepMind, and TRI. Deployment is facilitated via Python and Hugging Face tools, with flash attention support for efficient inference. -
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wav2vec2-large-xlsr-53-russian
Russian ASR model fine-tuned on Common Voice and CSS10 datasets
...It achieves a Word Error Rate (WER) of 13.3% and Character Error Rate (CER) of 2.88% on the Common Voice test set, with even better results when used with a language model. The model supports both PyTorch and JAX and is compatible with the Hugging Face Transformers and HuggingSound libraries. It is ideal for Russian voice transcription tasks in research, accessibility, and interface development. The training was made possible with compute support from OVHcloud, and the training scripts are publicly available for replication. -
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...What if there were a collapse of infrastructure? How well would your lifestyle hold up? What changes can you realistically make to your life so that your household is resilient in the face of catastrophe?
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WHAY is a Video-based Face Recognition tool written in MATLAB. It aims to exploits PCA recognizing as better as possible and tests the limits of this approach.
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Invasion TD is a Tron meets Tower Defense game, where a computer is invaded by viruses and the player must defend critical parts of his system. Player's face-off by sending waves of viruses into their opponent's system.
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Reverse Image Search Anywhere
4-engine reverse image search that works on Instagram & Pinterest
...Right-click any image to search Google Lens, Yandex, Bing Visual and TinEye at once, or pick a single engine. You can also paste an image (Ctrl/Cmd+V), drag to capture a region of the page, or crop and run a dedicated "Search this face" flow. Private by default: ordinary searches go straight from your browser to the engine and never touch our servers. Where a site hides the image URL, the extension uploads that one image to mint a temporary link; it auto-deletes within about an hour and is never sold or used for training. No account, no tracking, no telemetry. ... -
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BLEURT-20-D12
Custom BLEURT model for evaluating text similarity using PyTorch
BLEURT-20-D12 is a PyTorch implementation of BLEURT, a model designed to assess the semantic similarity between two text sequences. It serves as an automatic evaluation metric for natural language generation tasks like summarization and translation. The model predicts a score indicating how similar a candidate sentence is to a reference sentence, with higher scores indicating greater semantic overlap. Unlike standard BLEURT models from TensorFlow, this version is built from a custom PyTorch... -
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layoutlm-base-uncased
Multimodal Transformer for document image understanding and layout
layoutlm-base-uncased is a multimodal transformer model developed by Microsoft for document image understanding tasks. It incorporates both text and layout (position) features to effectively process structured documents like forms, invoices, and receipts. This base version has 113 million parameters and is pre-trained on 11 million documents from the IIT-CDIP dataset. LayoutLM enables better performance in tasks where the spatial arrangement of text plays a crucial role. The model uses a... -
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bge-small-en-v1.5
Compact English sentence embedding model for semantic search tasks
...The model is optimized for speed and efficiency, making it suitable for resource-constrained environments. It is compatible with popular libraries such as FlagEmbedding, Sentence-Transformers, and Hugging Face Transformers. The model achieves competitive results on the MTEB benchmark, especially in retrieval and classification tasks. With only 33.4M parameters, it provides a strong balance of accuracy and performance for English-only use cases. -
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t5-base
Flexible text-to-text transformer model for multilingual NLP tasks
t5-base is a pre-trained transformer model from Google’s T5 (Text-To-Text Transfer Transformer) family that reframes all NLP tasks into a unified text-to-text format. With 220 million parameters, it can handle a wide range of tasks, including translation, summarization, question answering, and classification. Unlike traditional models like BERT, which output class labels or spans, T5 always generates text outputs. It was trained on the C4 dataset, along with a variety of supervised NLP... -
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Qwen2.5-VL-3B-Instruct
Qwen2.5-VL-3B-Instruct: Multimodal model for chat, vision & video
Qwen2.5-VL-3B-Instruct is a 3.75 billion parameter multimodal model by Qwen, designed to handle complex vision-language tasks in both image and video formats. As part of the Qwen2.5 series, it supports image-text-to-text generation with capabilities like chart reading, object localization, and structured data extraction. The model can serve as an intelligent visual agent capable of interacting with digital interfaces and understanding long-form videos by dynamically sampling resolution and... -
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bart-large-cnn
Summarization model fine-tuned on CNN/DailyMail articles
...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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mms-300m-1130-forced-aligner
CTC-based forced aligner for audio-text in 158 languages
mms-300m-1130-forced-aligner is a multilingual forced alignment model based on Meta’s MMS-300M wav2vec2 checkpoint, adapted for Hugging Face’s Transformers library. It supports forced alignment between audio and corresponding text across 158 languages, offering broad multilingual coverage. The model enables accurate word- or phoneme-level timestamping using Connectionist Temporal Classification (CTC) emissions. Unlike other tools, it provides significant memory efficiency compared to the... -
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Qwen2.5-VL-7B-Instruct
Multimodal 7B model for image, video, and text understanding tasks
Qwen2.5-VL-7B-Instruct is a multimodal vision-language model developed by the Qwen team, designed to handle text, images, and long videos with high precision. Fine-tuned from Qwen2.5-VL, this 7-billion-parameter model can interpret visual content such as charts, documents, and user interfaces, as well as recognize common objects. It supports complex tasks like visual question answering, localization with bounding boxes, and structured output generation from documents. The model is also... -
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Qwen2.5-14B-Instruct
Powerful 14B LLM with strong instruction and long-text handling
Qwen2.5-14B-Instruct is a powerful instruction-tuned language model developed by the Qwen team, based on the Qwen2.5 architecture. It features 14.7 billion parameters and is optimized for tasks like dialogue, long-form generation, and structured output. The model supports context lengths up to 128K tokens and can generate up to 8K tokens, making it suitable for long-context applications. It demonstrates improved performance in coding, mathematics, and multilingual understanding across over... -
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Bio_ClinicalBERT
ClinicalBERT model trained on MIMIC notes for clinical NLP tasks
Bio_ClinicalBERT is a domain-specific language model tailored for clinical natural language processing (NLP), extending BioBERT with additional training on clinical notes. It was initialized from BioBERT-Base v1.0 and further pre-trained on all clinical notes from the MIMIC-III database (~880M words), which includes ICU patient records. The training focused on improving performance in tasks like named entity recognition and natural language inference within the healthcare domain. Notes were...
