Hugging Face's logo

mchochowski
/
test-model

Image Classification
Transformers
resnet
Inference Endpoints
Model card Files Community
test-model / README.md
mchochowski's picture
mchochowski
Update README.md
044c615
preview code
|
raw
history blame contribute delete
4.6 kB
metadata
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.

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.

!pip install validators matplotlib

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.

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.

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.

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.

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 and/or NGC

References