Spaces:
Runtime error
Runtime error
Update app.py
Browse files
app.py
CHANGED
@@ -4,7 +4,7 @@ import torchvision.transforms as transforms
|
|
4 |
|
5 |
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
|
6 |
|
7 |
-
resneXt = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', '
|
8 |
utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_processing_utils')
|
9 |
|
10 |
resneXt.eval().to(device)
|
@@ -34,10 +34,10 @@ def inference(img):
|
|
34 |
|
35 |
return results
|
36 |
|
37 |
-
title="ResNeXt101"
|
38 |
-
description="Gradio demo for ResNeXt101,
|
39 |
|
40 |
-
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/
|
41 |
|
42 |
examples=[['food.jpeg']]
|
43 |
gr.Interface(inference,gr.inputs.Image(type="pil"),"text",title=title,description=description,article=article,examples=examples).launch(enable_queue=True)
|
|
|
4 |
|
5 |
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
|
6 |
|
7 |
+
resneXt = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_se_resnext101_32x4d')
|
8 |
utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_processing_utils')
|
9 |
|
10 |
resneXt.eval().to(device)
|
|
|
34 |
|
35 |
return results
|
36 |
|
37 |
+
title="SE-ResNeXt101"
|
38 |
+
description="Gradio demo for SE-ResNeXt101, ResNeXt with Squeeze-and-Excitation module added, trained with mixed precision using Tensor Cores. To use it, simply upload your image or click on one of the examples below. Read more at the links below"
|
39 |
|
40 |
+
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1709.01507'>Squeeze-and-Excitation Networks</a> | <a href='https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/se-resnext101-32x4d'>Github Repo</a></p>"
|
41 |
|
42 |
examples=[['food.jpeg']]
|
43 |
gr.Interface(inference,gr.inputs.Image(type="pil"),"text",title=title,description=description,article=article,examples=examples).launch(enable_queue=True)
|