This is a video understanding model based on the VideoMAE architecture. It was pre-trained on the Kinetics dataset and fine-tuned on an unknown dataset that may be related to sign language recognition. The model achieved an accuracy of 78.11% on the evaluation set and is suitable for video classification tasks.

Computer VisionTransformers

This is a video understanding model fine-tuned based on the VideoMAE-base architecture, specifically optimized for sign language recognition tasks. The model was trained on the WLASL dataset for 200 epochs, using the TimeSformer architecture to process video sequences.

Computer VisionTransformers

This is a video understanding model fine-tuned based on the VideoMAE-base architecture, specifically optimized for sign language recognition tasks. The model was trained on the WLASL100 dataset for 200 epochs and has the ability to recognize video actions.

Computer VisionTransformers

This is a video understanding model fine-tuned on an unknown dataset based on the VideoMAE-base architecture, specifically designed for sign language recognition tasks. The model achieved an accuracy of 18.64% after 20 training epochs.

Computer VisionTransformers

This is a video understanding model based on the VideoMAE architecture. It has been fine-tuned on the basis of pre-training on the Kinetics dataset and is specifically designed for sign language recognition tasks. The model's performance on the evaluation set needs improvement, with an accuracy of 0.0010.

Computer VisionTransformers

This is an automatic speech recognition model optimized for Apple silicon chip devices. By converting to the MLX framework and quantizing to the FP8 format, it enables fast on-device speech transcription on Apple devices. The model is fine-tuned for verbatim accuracy and is particularly suitable for scenarios requiring high-precision transcription.

Audio ProcessingMlxOther

This is an optimized version based on the original DeepSeek-OCR model, specifically an OCR model that supports inference on Apple Metal Performance Shaders (MPS) and CPU. It can extract text from images and convert it into a structured format, supporting multilingual document recognition.

MultimodalTransformersOther

GigaAM-v3 is a basic model for Russian automatic speech recognition based on the Conformer architecture, with 220 - 240 million parameters. It is the third-generation model in the GigaAM series, pre-trained on 700,000 hours of Russian speech data using the HuBERT-CTC objective, providing state-of-the-art performance in a wide range of Russian ASR fields.

Audio ProcessingPytorchMultiple Languages

This is a video action recognition model fine-tuned on the WLASL dataset based on the VideoMAE-base architecture, specifically optimized for sign language recognition tasks, achieving an accuracy of 48.22% on the evaluation set.

Computer VisionTransformers

This is an English automatic speech recognition model developed by Applied Brain Research (ABR) based on the state space model (SSM). It has approximately 19 million parameters and can efficiently and accurately transcribe English speech into text. The model performs excellently on multiple benchmark datasets, with an average word error rate of only 10.61%. It supports real-time speech recognition and can run on low-cost hardware.

Audio ProcessingTransformersEnglish

This is a video action recognition model fine-tuned on the WLASL dataset based on the VideoMAE-Base architecture. After 200 epochs of training, it achieved a top-1 accuracy of 52.96% and a top-5 accuracy of 79.88% on the evaluation set, specifically designed for sign language action recognition tasks.

Computer VisionTransformers

This is a character detection model specifically optimized for Japanese video game text. It adopts the Pareto optimal design and achieves the best balance between accuracy and latency. The model redefines text recognition as a character detection task and is trained on Japanese video game data, achieving advanced recognition performance.

Computer VisionOnnxMultiple Languages

This is a fine-tuned Whisper speech recognition model based on the unsloth/whisper-large-v3-turbo architecture. It uses the Unsloth and Huggingface TRL libraries to achieve 2x training acceleration, significantly improving training efficiency.

Audio ProcessingTransformersEnglish

This is an image text recognition model based on DeepSeek-OCR, which specifically solves the compatibility issues in the latest version of the transformers library, enabling the model to run smoothly in the latest versions such as transformers v4.57.1.

MultimodalTransformersEnglish

This model is an automatic speech recognition model fine-tuned for Swahili based on the OpenAI Whisper-small model. It was trained on the Swahili part of the FLEURS-SLU dataset, significantly improving the transcription accuracy of Swahili. The word error rate was reduced by 68% compared to the base model.

Audio ProcessingTransformersOther

LightOnOCR-1B-1025 is an OCR model based on image-to-text conversion. It uses a parameter scale of 1B and is optimized through imatrix quantization. It is specifically designed to extract and recognize text content from images.

MultimodalGguf

A quantized version of LightOnOCR-1B-1025, specifically designed for image-to-text tasks and widely used in fields such as document understanding and visual language processing. This model supports multiple European languages and is suitable for scenarios such as OCR, PDF processing, and table recognition.

MultimodalGgufMultiple Languages

i3-80M is an innovative hybrid architecture language model that combines the advantages of convolutional/recurrent layers and full attention layers. The model adopts RWKV-style time mixing and Mamba state space dynamics to achieve efficient sequence processing in the early layers and uses the standard multi-head attention mechanism in the deep layers for complex pattern recognition.

Natural Language ProcessingTransformersEnglish

DeepSeek-OCR-8bit is an MLX format version converted from the DeepSeek-OCR model. It is a visual language model specifically optimized for Apple chips, supporting multilingual OCR recognition and image text understanding tasks.

MultimodalMlxOther

DeepSeek-OCR-6bit is an MLX format version converted from the DeepSeek-OCR model, specifically optimized for Apple chips. This is a vision-language model with powerful optical character recognition capabilities, capable of extracting and recognizing text information from images.

MultimodalMlxOther