Search Results for "classification"
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Showing 29 open source projects for "classification"
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DINOv3
Reference PyTorch implementation and models for DINOv3
...The model supports multiple backbone architectures, including Vision Transformers (ViT), and can handle larger image resolutions with improved stability during training. The learned embeddings generalize robustly across tasks like classification, retrieval, and segmentation without fine-tuning, showing state-of-the-art transfer performance among self-supervised models. -
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MobileCLIP
Implementation of "MobileCLIP" CVPR 2024
MobileCLIP is a family of efficient image-text embedding models designed for real-time, on-device retrieval and zero-shot classification. The repo provides training, inference, and evaluation code for MobileCLIP models trained on DataCompDR, and for newer MobileCLIP2 models trained on DFNDR. It includes an iOS demo app and Core ML artifacts to showcase practical, offline photo search and classification on iPhone-class hardware. Project notes highlight latency/accuracy trade-offs, with MobileCLIP2 variants matching or surpassing larger baselines at notably lower parameter counts and runtime on mobile devices. ... -
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CLIP
CLIP, Predict the most relevant text snippet given an image
CLIP (Contrastive Language-Image Pretraining) is a neural model that links images and text in a shared embedding space, allowing zero-shot image classification, similarity search, and multimodal alignment. It was trained on large sets of (image, caption) pairs using a contrastive objective: images and their matching text are pulled together in embedding space, while mismatches are pushed apart. Once trained, you can give it any text labels and ask it to pick which label best matches a given image—even without explicit training for that classification task. ... -
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OpenAI Privacy Filter
Bidirectional token-classification model for identifiable info
OpenAI Privacy Filter is an open-weight machine learning model designed to detect and mask personally identifiable information in text with high efficiency and contextual awareness. It operates as a bidirectional token classification system that labels sensitive data in a single forward pass rather than generating text sequentially, enabling fast processing for large datasets. The model supports long-context inputs, allowing it to analyze extensive documents without chunking, which improves consistency in redaction tasks. It can run locally on standard hardware, ensuring that sensitive information never leaves the user’s environment and supporting privacy-first workflows. ... -
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Qwen2-Audio
Repo of Qwen2-Audio chat & pretrained large audio language model
...It supports two major modes: Voice Chat (interactive voice only input) and Audio Analysis (audio + text instructions), with both base and instruction-tuned models. It is evaluated on many benchmarks (speech recognition, translation, sound classification, emotion, etc.), and offers pretrained models (e.g. 7B) released via ModelScope and Hugging Face. Code & examples provided with Hugging Face transformers, and usage via AutoProcessor, model classes etc. High performance on many standard benchmarks: ASR, speech-emotion recognition, vocal sound classification, speech translation etc. -
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Kimi-Audio
Audio foundation model excelling in audio understanding
Kimi-Audio is an ambitious open-source audio foundation model designed to unify a wide array of audio processing tasks — from speech recognition and audio understanding to generative conversation and sound event classification — within a single cohesive architecture. Instead of fragmenting work across specialized models, Kimi-Audio handles automatic speech recognition (ASR), audio question answering, automatic audio captioning, speech emotion recognition, and audio-to-text chat in one system, enabling developers to build rich, multimodal audio applications without stitching together disparate components. ... -
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DINOv2
PyTorch code and models for the DINOv2 self-supervised learning
...It builds on the DINO idea of student–teacher distillation and adapts it to modern Vision Transformer backbones with a carefully tuned recipe for data augmentation, optimization, and multi-crop training. The core promise is that a single pretrained backbone can transfer well to many downstream tasks—from linear probing on classification to retrieval, detection, and segmentation—often requiring little or no fine-tuning. The repository includes code for training, evaluating, and feature extraction, with utilities to run k-NN or linear evaluation baselines to assess representation quality. Pretrained checkpoints cover multiple model sizes so practitioners can trade accuracy for speed and memory depending on their deployment constraints. -
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Qwen3 Embedding
Designed for text embedding and ranking tasks
...It achieves state-of-the-art performance on benchmarks like MTEB (Multilingual Text Embedding Benchmark) and supports instruction-aware embedding (i.e. embedding task instructions along with queries) and flexible embedding/vector dimension definitions. It is meant for tasks such as text retrieval, classification, clustering, bitext mining, and code retrieval. -
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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... -
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GPT-2 Output Dataset
Dataset of GPT-2 outputs for research in detection, biases, and more
...The repository provides scripts and metadata for working with the dataset, with the goal of supporting research in areas like detection, evaluation of text coherence, and analysis of generative models. While no active development is expected, the dataset remains a useful benchmark for tasks involving text classification, style analysis, and generative model evaluation. -
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FastViT
This repository contains the official implementation of research
...The models use lightweight attention and carefully engineered blocks to minimize token mixing costs while preserving representation power. Training and inference recipes highlight straightforward integration into common vision tasks such as classification, detection, and segmentation. The codebase provides reference implementations and checkpoints that make it easy to evaluate or fine-tune on downstream datasets. In practice, FastViT offers drop-in backbones that reduce compute and memory pressure without exotic training tricks. -
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RoBERTa for Chinese
RoBERTa Chinese pre-training model: RoBERTa for Chinese
...The repository also describes whole word masking for Chinese and provides examples for loading and fine-tuning models on sentence-pair matching tasks. Overall, it is a useful pretrained model resource for developers who want stronger Chinese BERT-style representations for classification, matching, reading comprehension, and related NLP tasks. -
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Mask2Former
Code release for "Masked-attention Mask Transformer
Mask2Former is a unified segmentation architecture that handles semantic, instance, and panoptic segmentation with one model and one training recipe. Its core idea is to cast segmentation as mask classification: a transformer decoder predicts a set of mask queries, each with an associated class score, eliminating the need for task-specific heads. A pixel decoder fuses multi-scale features and feeds masked attention in the transformer so each query focuses computation on its current spatial support. This leads to accurate masks with sharp boundaries and strong small-object performance while remaining efficient on high-resolution inputs. ... -
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MAE (Masked Autoencoders)
PyTorch implementation of MAE
...The encoder processes only the visible patches, while a lightweight decoder reconstructs the full image—making pretraining computationally efficient. After pretraining, the encoder serves as a powerful backbone for downstream tasks like image classification, segmentation, and detection, achieving top performance with minimal fine-tuning. The repository provides pretrained models, fine-tuning scripts, evaluation protocols, and visualization tools for reconstruction quality and learned features. -
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TimeSformer
The official pytorch implementation of our paper
...The official implementation in PyTorch provides configurations, pretrained models, and training scripts that make it straightforward to evaluate or fine-tune on video datasets. TimeSformer was influential in showing that pure transformer architectures—without convolutional backbones—can perform strongly on video classification tasks. Its flexible attention design allows experimenting with different factoring (spatial-then-temporal, joint, etc.) to trade off compute, memory, and accuracy. -
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StarSpace
Learning embeddings for classification, retrieval and ranking
...The library supports a variety of tasks (text classification, nearest-neighbor search, recommendation, entity linking) with simple configuration. It includes efficient batching, negative sampling strategies, and on-the-fly embedding updates. -
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MAML-Pytorch
Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning
...The project also notes that MAML can be difficult to train and presents the implementation as a practical starting point for research. Overall, it is useful for students and researchers who want to study fast adaptation, few-shot classification, and gradient-based meta-learning in PyTorch. -
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MUSE
A library for Multilingual Unsupervised or Supervised word Embeddings
...The training and evaluation pipeline is lightweight and fast, so experimenting with different languages or initialization strategies is easy. Beyond dictionary induction, the learned embeddings are often used as building blocks for downstream tasks like classification, retrieval, or machine translation. -
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fashion-clip
CLIP model fine-tuned for zero-shot fashion product classification
FashionCLIP is a domain-adapted CLIP model fine-tuned specifically for the fashion industry, enabling zero-shot classification and retrieval of fashion products. Developed by Patrick John Chia and collaborators, it builds on the CLIP ViT-B/32 architecture and was trained on over 800K image-text pairs from the Farfetch dataset. The model learns to align product images and descriptive text using contrastive learning, enabling it to perform well across various fashion-related tasks without additional supervision. ... -
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roberta-base
Robust BERT-based model for English with improved MLM training
...It captures contextual representations of language by masking 15% of input tokens and predicting them. RoBERTa is designed to be fine-tuned for a wide range of NLP tasks such as classification, QA, and sequence labeling, achieving strong performance on the GLUE benchmark and other downstream applications. -
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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 benchmarks, using both unsupervised denoising and supervised objectives. The model supports multiple languages, including English, French, Romanian, and German. ... -
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t5-small
T5-Small: Lightweight text-to-text transformer for NLP tasks
T5-Small is a lightweight variant of the Text-To-Text Transfer Transformer (T5), designed to handle a wide range of NLP tasks using a unified text-to-text approach. Developed by researchers at Google, this model reframes all tasks—such as translation, summarization, classification, and question answering—into the format of input and output as plain text strings. With only 60 million parameters, T5-Small is compact and suitable for fast inference or deployment in constrained environments. It was pretrained on the C4 dataset using both unsupervised denoising and supervised learning on tasks like sentiment analysis, NLI, and QA. ... -
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CLIP-ViT-bigG-14-laion2B-39B-b160k
CLIP ViT-bigG/14: Zero-shot image-text model trained on LAION-2B
...Developed by LAION and trained by Mitchell Wortsman on Stability AI’s compute infrastructure, it pairs a ViT-bigG/14 vision transformer with a text encoder to perform contrastive learning on image-text pairs. This model excels at zero-shot image classification, image-to-text and text-to-image retrieval, and can be adapted for tasks such as image captioning or generation guidance. It achieves an impressive 80.1% top-1 accuracy on ImageNet-1k without any fine-tuning, showcasing its robustness in open-domain settings. Its training dataset is uncurated and web-sourced, meaning it reflects the biases and risks of large-scale internet data. ... -
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layoutlm-base-uncased
Multimodal Transformer for document image understanding and layout
...It achieves state-of-the-art results in form understanding and information extraction benchmarks. This model is particularly useful for document AI applications like document classification, question answering, and named entity recognition. -
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bge-small-en-v1.5
Compact English sentence embedding model for semantic search tasks
...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.
