
### 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

```

{"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"}]}
transformers image-classification resnet dataset:imagenet arxiv:1512.03385 arxiv:1502.01852 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #image-classification #resnet #dataset-imagenet #arxiv-1512.03385 #arxiv-1502.01852 #license-apache-2.0 #endpoints_compatible #region-us 


### Model Description

The *ResNet50 v1.5* model is a modified version of the original ResNet50 v1 model.

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

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 using TorchScript, ONNX Runtime or TensorRT as an execution backend. For details check NGC


### 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.




Load the model pretrained on IMAGENET dataset.


Prepare sample input data.


Run inference. Use 'pick_n_best(predictions=output, n=topN)' helepr function to pick N most probably hypothesis according to the model.


Display the result.


### Details
For detailed information on model input and output, training recipies, inference and performance visit:
github
and/or NGC

### References

 - Original ResNet50 v1 paper
 - Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
 - model on github
 - model on NGC
 - pretrained model on NGC
 


### Model Description

The *ResNet50 v1.5* model is a modified version of the original ResNet50 v1 model.

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

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 using TorchScript, ONNX Runtime or TensorRT as an execution backend. For details check NGC ### 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.




Load the model pretrained on IMAGENET dataset.


Prepare sample input data.


Run inference. Use 'pick_n_best(predictions=output, n=topN)' helepr function to pick N most probably hypothesis according to the model.


Display the result. ### Details
For detailed information on model input and output, training recipies, inference and performance visit:
github
and/or NGC ### References

 - Original ResNet50 v1 paper
 - Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
 - model on github
 - model on NGC
 - pretrained model on NGC
TAGS
#transformers #image-classification #resnet #dataset-imagenet #arxiv-1512.03385 #arxiv-1502.01852 #license-apache-2.0 #endpoints_compatible #region-us 
 ### Model Description

The *ResNet50 v1.5* model is a modified version of the original ResNet50 v1 model.

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

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 using TorchScript, ONNX Runtime or TensorRT as an execution backend. For details check NGC ### 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.




Load the model pretrained on IMAGENET dataset.


Prepare sample input data.


Run inference. Use 'pick_n_best(predictions=output, n=topN)' helepr function to pick N most probably hypothesis according to the model.


Display the result. ### Details
For detailed information on model input and output, training recipies, inference and performance visit:
github
and/or NGC ### References

 - Original ResNet50 v1 paper
 - Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
 - model on github
 - model on NGC
 - pretrained model on NGC
passage: TAGS
#transformers #image-classification #resnet #dataset-imagenet #arxiv-1512.03385 #arxiv-1502.01852 #license-apache-2.0 #endpoints_compatible #region-us 
### Model Description

The *ResNet50 v1.5* model is a modified version of the original ResNet50 v1 model.

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

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 using TorchScript, ONNX Runtime or TensorRT as an execution backend. For details check NGC### 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.




Load the model pretrained on IMAGENET dataset.


Prepare sample input data.


Run inference. Use 'pick_n_best(predictions=output, n=topN)' helepr function to pick N most probably hypothesis according to the model.


Display the result.