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+---
+layout: hub_detail
+background-class: hub-background
+body-class: hub
+title: LR-ASPP
+summary: Lite Reduced Atrous Spatial Pyramid Pooling model with MobileNetV3-Large backbone.
+category: researchers
+image: lraspp2.png
+author: Pytorch Team
+tags: [vision, scriptable]
+github-link: https://github.com/pytorch/vision/blob/main/torchvision/models/segmentation/lraspp.py
+github-id: pytorch/vision
+featured_image_1: deeplab1.png
+featured_image_2: lraspp2.png
+accelerator: cuda-optional
+order: 10
+---
+
+```python
+import torch
+model = torch.hub.load('pytorch/vision:v0.10.0', 'lraspp_mobilenet_v3_large', pretrained=True)
+model.eval()
+```
+
+All pre-trained models expect input images normalized in the same way,
+i.e. mini-batches of 3-channel RGB images of shape `(N, 3, H, W)`, where `N` is the number of images, `H` and `W` are expected to be at least `224` pixels.
+The images have to be loaded in to a range of `[0, 1]` and then normalized using `mean = [0.485, 0.456, 0.406]`
+and `std = [0.229, 0.224, 0.225]`.
+
+The model returns an `OrderedDict` with two Tensors that are of the same height and width as the input Tensor, but with 21 classes.
+`output['out']` contains the semantic masks, and `output['aux']` contains the auxillary loss values per-pixel. In inference mode, `output['aux']` is not useful.
+So, `output['out']` is of shape `(N, 21, H, W)`. More documentation can be found [here](https://pytorch.org/vision/stable/models.html#object-detection-instance-segmentation-and-person-keypoint-detection).
+
+
+```python
+# Download an example image from the pytorch website
+import urllib
+url, filename = ("https://github.com/pytorch/hub/raw/master/images/deeplab1.png", "deeplab1.png")
+try: urllib.URLopener().retrieve(url, filename)
+except: urllib.request.urlretrieve(url, filename)
+```
+
+```python
+# sample execution (requires torchvision)
+from PIL import Image
+from torchvision import transforms
+input_image = Image.open(filename)
+input_image = input_image.convert("RGB")
+preprocess = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+input_tensor = preprocess(input_image)
+input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+
+# move the input and model to GPU for speed if available
+if torch.cuda.is_available():
+    input_batch = input_batch.to('cuda')
+    model.to('cuda')
+
+with torch.no_grad():
+    output = model(input_batch)['out'][0]
+output_predictions = output.argmax(0)
+```
+
+The output here is of shape `(21, H, W)`, and at each location, there are unnormalized probabilities corresponding to the prediction of each class.
+To get the maximum prediction of each class, and then use it for a downstream task, you can do `output_predictions = output.argmax(0)`.
+
+Here's a small snippet that plots the predictions, with each color being assigned to each class (see the visualized image on the left).
+
+```python
+# create a color pallette, selecting a color for each class
+palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
+colors = torch.as_tensor([i for i in range(21)])[:, None] * palette
+colors = (colors % 255).numpy().astype("uint8")
+
+# plot the semantic segmentation predictions of 21 classes in each color
+r = Image.fromarray(output_predictions.byte().cpu().numpy()).resize(input_image.size)
+r.putpalette(colors)
+
+import matplotlib.pyplot as plt
+plt.imshow(r)
+# plt.show()
+```
+
+### Model Description
+
+LR-ASPP description here.
+
+Their accuracies of the pre-trained models evaluated on COCO val2017 dataset are listed below.
+
+|  Model structure                |   Mean IOU  | Global Pixelwise Accuracy |
+| -----------------               | ----------- | --------------------------|
+|  lraspp_mobilenet_v3_large      |   57.9      |   91.2                    |
+
+### Resources
+
+ - [Searching for MobileNetV3](https://arxiv.org/abs/1905.02244)
+ - [PyTorch Blog on Training LRSAPP Model](https://pytorch.org/blog/torchvision-mobilenet-v3-implementation)