pytorch

README.md

@@ -70,7 +70,7 @@ DenseNet-BC (L=250, k=24)|15.3M |**5.19** |3.62 | **19.64**|17.60
 DenseNet-BC (L=190, k=40)|25.6M |- |**3.46** | -|**17.18**
 
 ## ImageNet and Pretrained Models
-###Torch
+### Torch
 The Torch models are trained under the same setting as in [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch). The error rates shown are 224x224 1-crop test errors.
 
 | Network       |  Top-1 error | Torch Model |
@@ -80,17 +80,19 @@ The Torch models are trained under the same setting as in [fb.resnet.torch](http
 | DenseNet-201 (k=32)    | 22.5       | [Download (161.8MB)](https://drive.google.com/open?id=0B8ReS-sYUS-HaDdpNmlWRjJkd3c)       |
 | DenseNet-161 (k=48)    | 22.2       | [Download (230.8MB)](https://drive.google.com/open?id=0B8ReS-sYUS-HVXp2RExSTmMzZVU)
 
-###Caffe
+### Caffe
 For ImageNet pretrained Caffe models, please see https://github.com/shicai/DenseNet-Caffe from @shicai. Also, we would like to thank @szq0214 for help on Caffe models.
 
 
-###PyTorch
+### PyTorch
 In PyTorch, ImageNet pretrained models can be directly loaded by 
 
 ```import torchvision.models as models```
+
 ```densenet = models.densenet_161(pretrained=True)```
 
 For ImageNet training, customized models can be constructed by simply calling
+
 ```DenseNet(growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000)```. 
 
 See more details at http://pytorch.org/docs/torchvision/models.html?highlight=densenet and https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py.