2.现在启动 Label-Studio 网页服务: ⚠(如不使用vit-h的SAM后端请跳过此步)使用的推理后端是SAM的vit-h, 由于模型加载时间长,导致连接后端超时,需要设置以下环境变量。 具体可根据下载的SAM的权值名称判断,比如sam_vit_h_4b8939.pth 为 vit-h,sam...
SAM有三种模型可以选择,分别为sam_vit_l_0b3195、sam_vit_b_01ec64、sam_vit_h_4b8939。规模、效果以及需要的算力依次增加。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 sam_checkpoint="checkpoint/sam_vit_h_4b8939.pth"model_type="vit_h"device="cuda" 加载模型 使用sam_model_registry 加载...
# wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth PS: 如果您使用Windows环境,请忽略 wget 命令,手动下载 wget 的目标文件(复制 url 到浏览器或下载工具中) 例如: https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth 安装Label-Studio 和 label-studio-...
fix_mask_de: False ## mask解码器可以微调 ckpt_path: 'sam_ckpt/sam_vit_b_01ec64.pth' ## 使用sam_vit_b作为预训练模型 class_num: 21 # 20 + 1 model_type: 'vit_b' # type should be in [vit_h, vit_b, vit_l, default] 数据集参数配置 dataset: ## name 的名称决定了使用哪种方...
checkpoint = "sam_vit_b_01ec64.pth" model_url = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth" model_type = "vit_b" download_file(model_url) 加载模型 from segment_anything import sam_model_registry
checkpoint = "sam_vit_b_01ec64.pth" model_url = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth" model_type = "vit_b" download_file(model_url) 向右滑动查看完整代码 加载模型 from segment_anything import sam_model_registry ...
sam_checkpoint = "./models/sam_vit_b_01ec64.pth" model_type = "vit_b" device = "cpu" sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) sam.to(device=device) mask_generator = SamAutomaticMaskGenerator(sam) masks = mask_generator.generate(image) ...
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth 导出onnx python -m samexporter.export_encoder --checkpoint ./sam_vit_b_01ec64.pth --output vit_b_encoder_noeinsum.onnx --model-type vit_b --opset 11 python -m samexporter.export_decoder --checkpoint ./sam...
sam = sam_model_registry["vit_b"](checkpoint="./sam_vit_b_01ec64.pth") # 3. Put the model to the SamPredictor helper object predictor = SamPredictor(sam) # 4. Encode the image to embeddings. predictor.set_image(img) # 5. Prepare the prompt ...
Download the SAM vit_b model from [here](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth) and place it in the 'segment_anything' folder. @@ -34,6 +37,7 @@ dataset ``` ## Training ### Prompt mode Single gpu: ```shell python train.py --config configs/...