库名称: segmentation-models-pytorch 许可证: mit 管道标签: 图像分割 标签:

• 模型中心混合
• pytorch模型中心混合
• 分割模型-pytorch
• 语义分割
• pytorch
• dpt 语言:
• python

DPT模型卡片

目录:

• 加载训练好的模型
• 模型初始化参数
• 数据集

加载训练好的模型

1. 安装依赖项。

pip install -U segmentation_models_pytorch albumentations

2. 运行推理。

import torch
import requests
import numpy as np
import albumentations as A
import segmentation_models_pytorch as smp

from PIL import Image

device = "cuda" if torch.cuda.is_available() else "cpu"

# 加载预训练模型和预处理函数
checkpoint = "smp-hub/dpt-large-ade20k"
model = smp.from_pretrained(checkpoint).eval().to(device)
preprocessing = A.Compose.from_pretrained(checkpoint)

# 加载图像
url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
image = Image.open(requests.get(url, stream=True).raw)

# 预处理图像
np_image = np.array(image)
normalized_image = preprocessing(image=np_image)["image"]
input_tensor = torch.as_tensor(normalized_image)
input_tensor = input_tensor.permute(2, 0, 1).unsqueeze(0)  # HWC -> BCHW
input_tensor = input_tensor.to(device)

# 执行推理
with torch.no_grad():
    output_mask = model(input_tensor)

# 后处理掩码
mask = torch.nn.functional.interpolate(
    output_mask, size=(image.height, image.width), mode="bilinear", align_corners=False
)
mask = mask.argmax(1).cpu().numpy()  # 对预测类别（通道维度）进行argmax

模型初始化参数

model_init_params = {
    "encoder_name": "tu-vit_large_patch16_384",
    "encoder_depth": 4,
    "encoder_weights": None,
    "encoder_output_indices": None,
    "decoder_intermediate_channels": (256, 512, 1024, 1024),
    "decoder_fusion_channels": 256,
    "dynamic_img_size": True,
    "in_channels": 3,
    "classes": 150,
    "activation": None,
    "aux_params": None
}

数据集

数据集名称: ADE20K

更多信息

• 库: https://github.com/qubvel/segmentation_models.pytorch
• 文档: https://smp.readthedocs.io/en/latest/

此模型已使用PytorchModelHubMixin推送到Hub。