Add basic mnasnet info

Author: oke-aditya

images/mnasnet1.png

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+---
+layout: hub_detail
+background-class: hub-background
+body-class: hub
+title: MNasNet
+summary: Neural Architecture Search based models for Mobile devices
+category: researchers
+image: mnasnet1.png
+author: Pytorch Team
+tags: [vision, scriptable]
+github-link: https://github.com/pytorch/vision/blob/main/torchvision/models/mnasnet.py
+github-id: pytorch/vision
+featured_image_1: mnasnet1.png
+featured_image_2: no-image
+accelerator: cuda-optional
+order: 10
+---
+
+```python
+import torch
+model = torch.hub.load('pytorch/vision:v0.10.0', 'mnasnet0_5', pretrained=True)
+# or
+# model = torch.hub.load('pytorch/vision:v0.10.0', 'mnasnet1_0', 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 `(3 x H x W)`, where `H` and `W` are expected to be at least `224`.
+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]`.
+
+Here's a sample execution.
+
+```python
+# Download an example image from the pytorch website
+import urllib
+url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
+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)
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    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)
+# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
+print(output[0])
+# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
+probabilities = torch.nn.functional.softmax(output[0], dim=0)
+print(probabilities)
+```
+
+```
+# Download ImageNet labels
+!wget https://github.com/raw/pytorch/hub/master/imagenet_classes.txt
+```
+
+```
+# Read the categories
+with open("imagenet_classes.txt", "r") as f:
+    categories = [s.strip() for s in f.readlines()]
+# Show top categories per image
+top5_prob, top5_catid = torch.topk(probabilities, 5)
+for i in range(top5_prob.size(0)):
+    print(categories[top5_catid[i]], top5_prob[i].item())
+```
+
+### Model Description
+
+The MnasNet v3 architecture is based on some stuff.
+
+|  Model structure  | Top-1 error | Top-5 error |
+|  ---------------  | ----------- | ----------- |
+|  mnasnet0_5       | 26.54       | 8.49        |
+|  mnasnet1_0       | 32.26       | 12.51       |
+
+
+### References
+
+ - [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://arxiv.org/abs/1807.11626)