readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - resnet
  datasets:
  - imagenet
  widget:
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
    example_title: Tiger
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
    example_title: Teapot
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
    example_title: Palace
  ---
  
  ### Model Description
  
  The ***ResNet50 v1.5*** model is a modified version of the [original ResNet50 v1 model](https://arxiv.org/abs/1512.03385).
  
  The difference between v1 and v1.5 is that, in the bottleneck blocks which requires
  downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.
  
  This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a smallperformance drawback (\~5% imgs/sec).
  
  The model is initialized as described in [Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification](https://arxiv.org/pdf/1502.01852.pdf)
  
  This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results over 2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.
  
  Note that the ResNet50 v1.5 model can be deployed for inference on the [NVIDIA Triton Inference Server](https://github.com/NVIDIA/trtis-inference-server) using TorchScript, ONNX Runtime or TensorRT as an execution backend. For details check [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnet_for_triton_from_pytorch)
  
  
  ### Example
  
  In the example below we will use the pretrained ***ResNet50 v1.5*** model to perform inference on ***image*** and present the result.
  
  To run the example you need some extra python packages installed. These are needed for preprocessing images and visualization.
  ```python
  !pip install validators matplotlib
  ```
  
  ```python
  import torch
  from PIL import Image
  import torchvision.transforms as transforms
  import numpy as np
  import json
  import requests
  import matplotlib.pyplot as plt
  import warnings
  warnings.filterwarnings('ignore')
  %matplotlib inline
  
  device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
  print(f'Using {device} for inference')
  ```
  
  Load the model pretrained on IMAGENET dataset.
  ```python
  resnet50 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)
  utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_processing_utils')
  
  resnet50.eval().to(device)
  ```
  
  Prepare sample input data.
  ```python
  uris = [
      'http://images.cocodataset.org/test-stuff2017/000000024309.jpg',
      'http://images.cocodataset.org/test-stuff2017/000000028117.jpg',
      'http://images.cocodataset.org/test-stuff2017/000000006149.jpg',
      'http://images.cocodataset.org/test-stuff2017/000000004954.jpg',
  ]
  
  batch = torch.cat(
      [utils.prepare_input_from_uri(uri) for uri in uris]
  ).to(device)
  ```
  
  Run inference. Use `pick_n_best(predictions=output, n=topN)` helepr function to pick N most probably hypothesis according to the model.
  ```python
  with torch.no_grad():
      output = torch.nn.functional.softmax(resnet50(batch), dim=1)
      
  results = utils.pick_n_best(predictions=output, n=5)
  ```
  
  Display the result.
  ```python
  for uri, result in zip(uris, results):
      img = Image.open(requests.get(uri, stream=True).raw)
      img.thumbnail((256,256), Image.ANTIALIAS)
      plt.imshow(img)
      plt.show()
      print(result)
  
  ```
  
  ### Details
  For detailed information on model input and output, training recipies, inference and performance visit:
  [github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/resnet50v1.5)
  and/or [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnet_50_v1_5_for_pytorch)
  
  ### References
  
   - [Original ResNet50 v1 paper](https://arxiv.org/abs/1512.03385)
   - [Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification](https://arxiv.org/pdf/1502.01852.pdf)
   - [model on github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/resnet50v1.5)
   - [model on NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnet_50_v1_5_for_pytorch)
   - [pretrained model on NGC](https://ngc.nvidia.com/catalog/models/nvidia:resnet50_pyt_amp)
   
  ```python
  
  ```
  