Showing 4 open source projects for "self code"

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  • 1
    DINOv2

    DINOv2

    PyTorch code and models for the DINOv2 self-supervised learning

    ...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.
    Downloads: 1 This Week
    Last Update:
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  • 2
    Antigravity Claude Proxy

    Antigravity Claude Proxy

    Proxy that exposes Antigravity provided claude / gemini models

    Antigravity Claude Proxy is a purpose-built proxy server that enables developers to interface with Claude models through a standardized RESTful API, allowing tools and workflows that expect generic HTTP APIs to operate on Anthropic’s Claude without native support. The project acts as a translation layer, receiving web requests in common formats (such as OpenAI-style endpoints) and forwarding them to Anthropic’s API in the required structure, while converting responses back into a familiar...
    Downloads: 3 This Week
    Last Update:
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  • 3
    ConvNeXt V2

    ConvNeXt V2

    Code release for ConvNeXt V2 model

    ...The result is a convnet that competes strongly with transformer architectures on recognition benchmarks while being efficient and hardware-friendly. The repository provides official PyTorch implementations for multiple model sizes (Atto, Femto, Pico, up through Huge), conversion from JAX weights, code for pretraining/fine-tuning, and pretrained checkpoints. It supports both self-supervised pretraining and supervised fine-tuning.
    Downloads: 1 This Week
    Last Update:
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  • 4
    wav2vec2-large-xlsr-53-portuguese

    wav2vec2-large-xlsr-53-portuguese

    Portuguese ASR model fine-tuned on XLSR-53 for 16kHz audio input

    wav2vec2-large-xlsr-53-portuguese is an automatic speech recognition (ASR) model fine-tuned on Portuguese using the Common Voice 6.1 dataset. It is based on Facebook’s wav2vec2-large-xlsr-53, a multilingual self-supervised learning model, and is optimized to transcribe Portuguese speech sampled at 16kHz. The model performs well without a language model, though adding one can improve word error rate (WER) and character error rate (CER). It achieves a WER of 11.3% (or 9.01% with LM) on Common...
    Downloads: 0 This Week
    Last Update:
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