Merge branch 'main' of https://huggingface.co/mchochowski/test-model into main

README.md

@@ -2,6 +2,8 @@
 license: apache-2.0
 tags:
 - image-classification
+- timm
+- resnet
 datasets:
 - imagenet
 widget:
@@ -12,3 +14,102 @@ widget:
 - 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
+
+```

config.json

@@ -0,0 +1,27 @@
+{
+  "architecture": "resnet50d",
+  "classifier": "fc",
+  "crop_pct": 0.875,
+  "first_conv": "conv1.0",
+  "input_size": [
+    3,
+    224,
+    224
+  ],
+  "interpolation": "bicubic",
+  "mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "num_classes": 1000,
+  "pool_size": [
+    7,
+    7
+  ],
+  "std": [
+    0.229,
+    0.224,
+    0.225
+  ]
+}