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ICCV 2023 论文和开源项目合集请戳—> github.com/amusi/ICCV20 扩大对比语言图像预训练(CLIP)对于增强视觉和多模态模型的能力至关重要。本文展示了EVA-CLIP-18B,这是迄今为止最大、最强大的开源CLIP模型,具有180亿个参数。在只看到60亿个训练样本的情况下,EVA-CLIP-18B在27个广泛认可的图像分类基准中平均达到了...
论文链接:https://arxiv.org/abs/2402.04252 模型和代码链接:https://github.com/baaivision/EVA/tree/master/EVA-CLIP-18B 技术亮点 Weak-to-strong 策略:以小教大,以弱引强 EVA-CLIP-18B沿用了 EVA 系列 weak-to-strong 的视觉模型scale up 策略,实现了视觉模型规模的渐进式扩增。该策略遵循“以小教大,以...
{ # unlimited seqlen # https://github.com/google-research/text-to-text-transfer-transformer/issues/273 # https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374 "context_length": "", "vocab_size": "vocab_size", "width": "d_model", "...
To facilitate open access and open research, we release the complete suite of EVA-CLIP to the community at https://github.com/baaivision/EVA/tree/master/EVA-CLIP. 展开 DOI: 10.48550/arXiv.2303.15389 年份: 2023 收藏 引用 批量引用 报错 分享 ...
# https://github.com/baaivision/EVA/tree/master/EVA-CLIP/rei/eva_clip # --- import math import os import warnings from functools import partial import torch import torch.nn as nn import torch.nn.functional as f try: warnings.filter...
这个错误通常表明fused_layer_norm_cuda模块没有正确安装。此模块是NVIDIA APEX库的一部分,用于加速PyTorch模型的某些操作。 如果未安装,提供安装fused_layer_norm_cuda模块的指导: 首先,您需要确保已安装NVIDIA APEX库。可以通过以下步骤安装: bash git clone https://github.com/NVIDIA/apex cd apex pip install ...
模型和代码链接:https://github.com/baaivision/EVA/tree/master/EVA-CLIP-18B 技术亮点 Weak-to-strong 策略:以小教大,以弱引强 EVA-CLIP-18B沿用了 EVA 系列 weak-to-strong 的视觉模型scale up 策略,实现了视觉模型规模的渐进式扩增。该策略遵循“以小教大,以弱引强”的规模扩增思想。
.github/workflows Merge branch 'main' into develop Mar 12, 2024 .vscode updatred Feb 27, 2024 src updated Mar 12, 2024 test updated Mar 12, 2024 .gitignore Initial commit Feb 27, 2024 LICENSE Initial commit Feb 27, 2024 Makefile Add Makefile, setup.py, and source code for Eva-CLIP ...
rei eva_clip model_configs __init__.py bpe_simple_vocab_16e6.txt.gz constants.py eva_vit_model.py factory.py hf_configs.py hf_model.py loss.py model.py modified_resnet.py openai.py pretrained.py rope.py timm_model.py tokenizer.py ...