要在Linux上部署Segment Anything Model,你可以按照以下步骤进行操作: 1. 安装Docker及Docker Compose 首先,确保你的Linux系统上已经安装了Docker和Docker Compose。如果还没有安装,你可以按照以下步骤进行安装。 对于Ubuntu系统,你可以使用以下命令安装Docker和Docker Compose:
Segment Anything (SA)即分割一切,论文的成果是最终发布了模型SAM,它无需fine-tune即可对图中任何物体进行分割,且能通过文本提示分割图像,效果可与有监督学习媲美。论文同时发布了超过1B图片,11M的mask标注的数据集SA-1B。 介绍 提示学习帮助大语言模型提升了处理zero-shot问题的能力;CLIP和ALIGN模型又提供了文本和...
backend.Dockerfile docker-compose.yaml pyproject.toml setup.py Breadcrumbs segment-anything-2 / backend.Dockerfile Latest commit haithamkhedr SAM2.1 aa9b872· Sep 29, 2024 HistoryHistory File metadata and controls Code Blame 64 lines (51 loc) · 2.02 KB Raw ARG BASE_IMAGE=pytorch/pytorch:2....
Segment Anything in Docker. A simple, easy to use Docker image for Meta's SAM2 with GUI support for displaying figures. Built on top of the SAM2 repo: https://github.com/facebookresearch/segment-anything-2 Quickstart This quickstart assumes you have access to an NVIDIA GPU. You should ha...
docker run -it --gpus=all -v /datas/work/zzq:/workspace 8fd9e4c5e7bc bash pip install opencv-python pycocotools matplotlib onnxruntime onnx -i pypi.tuna.tsinghua.edu.cn git clone github.com/facebookrese cd /workspace/SAM/segment-anything python scripts/amg.py --checkpoint sam_vit_h...
Segment Anything是前阵子大火的CV领域模型,之前也有尝试,只是没有整理。OK,让我们开始吧 一、拉取下载docker镜像 docker pull cnstark/pytorch:2.0.1-py3.9.17-cuda11.8.0-ubuntu20.04 二、安装SAM环境 docker run -it --gpus=all -v /datas/work/zzq:/workspace 8fd9e4c5e7bc bash ...
ADD https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt ${APP_ROOT}/checkpoints/sam2.1_hiera_large.pt WORKDIR ${APP_ROOT}/server # https://pythonspeed.com/articles/gunicorn-in-docker/ CMD gunicorn --worker-tmp-dir /dev/shm \ --worker-class gthread app...
3.2 Install without Docker 如果您想为Grounded-SAM构建本地GPU环境,您应该手动设置环境变量,如下所示: export AM_I_DOCKER=Falseexport BUILD_WITH_CUDA=Trueexport CUDA_HOME=/path/to/cuda-11.3/ 1. 2. 3. 安装Segment Anything: python-m pip install-e segment_anything ...
dockerfile: frontend.Dockerfile ports: - 7262:80 backend: image: sam2/backend build: context: . dockerfile: backend.Dockerfile ports: - 7263:5000 volumes: - ./demo/data/:/data/:rw environment: - SERVER_ENVIRONMENT=DEV - GUNICORN_WORKERS=1 # Inference API needs to ...
首先第一步就是要配置下segment anything的环境,这里我们不使用segment anything官方提供的服务跑了,因为确实要跑好多张,所以我们自己配一下。其实很简单就是安装一下pytorch gpu版本(显卡稍微差点的话可以用cpu的),然后把项目从github拉下来,在安装下他要求的库就ok了,网上有很多配置segment anything 的博客,这里我...