🚀 分割模型庫(segmentation - models - pytorch)之DPT模型
本項目是基於segmentation - models - pytorch
庫的圖像分割模型DPT,可用於語義分割任務,藉助該模型能方便地對圖像進行分割處理,在圖像分析等領域具有重要價值。
🚀 快速開始
加載預訓練模型
點擊下面的按鈕在Colab中運行示例:

安裝依賴
pip install -U segmentation_models_pytorch albumentations
運行推理
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"
checkpoint = "smp-hub/dpt-large-ade20k"
model = smp.from_pretrained(checkpoint).eval().to(device)
preprocessing = A.Compose.from_pretrained(checkpoint)
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)
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)
input_tensor = input_tensor.to(device)
with torch.no_grad():
output_mask = model(input_tensor)
mask = torch.nn.functional.interpolate(
output_mask, size=(image.height, image.width), mode="bilinear", align_corners=False
)
mask = mask.argmax(1).cpu().numpy()
💻 使用示例
基礎用法
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"
checkpoint = "smp-hub/dpt-large-ade20k"
model = smp.from_pretrained(checkpoint).eval().to(device)
preprocessing = A.Compose.from_pretrained(checkpoint)
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)
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)
input_tensor = input_tensor.to(device)
with torch.no_grad():
output_mask = model(input_tensor)
mask = torch.nn.functional.interpolate(
output_mask, size=(image.height, image.width), mode="bilinear", align_corners=False
)
mask = mask.argmax(1).cpu().numpy()
📚 詳細文檔
模型初始化參數
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
}
數據集
數據集名稱:ADE20K
更多信息
- 庫地址:https://github.com/qubvel/segmentation_models.pytorch
- 文檔地址:https://smp.readthedocs.io/en/latest/
本模型已使用 PytorchModelHubMixin 推送到模型中心。
📄 許可證
本項目採用MIT許可證。