fix some structure

nicolalandro_ntsnet-cub200_ntsnet.md

@@ -4,22 +4,23 @@ background-class: hub-background
 body-class: hub
 category: researchers
 title: ntsnet
-summary: a fined graned model for image classification.
+summary: classify birds using this fine-grained image classifier
 image: nts-net.png
 author: Moreno Carraffini and Nicola Landro
 tags: [vision]
-github-link: https://github.com/nicolalandro/ntsnet-cub200/archive/master.zip
+github-link: https://github.com/nicolalandro/ntsnet-cub200
 featured_image_1: Cub200Dataset.png
 featured_image_2: no-image
 accelerator: "cuda-optional"
 ---
 
 ```python
 import torch
-model = torch.hub.load('nicolalandro/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})
+model = torch.hub.load('nicolalandro/ntsnet-cub200', 'ntsnet', pretrained=True,
+                       **{'topN': 6, 'device':'cpu', 'num_classes': 200})
 ```
 
-### Example
+### Example Usage
 
 ```python
 from torchvision import transforms
@@ -52,60 +53,8 @@ with torch.no_grad():
     print('bird class:', model.bird_classes[pred_id])
 ```
 
-### How to train
-It is a particular model and if you want to train it use the follow:
-
-```
-import torch
-
-PROPOSAL_NUM = 6
-LR = 0.001
-WD = 1e-4
-
-net = torch.hub.load('nicolalandro/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})
-
-creterion = torch.nn.CrossEntropyLoss()
-
-# define optimizers
-raw_parameters = list(net.pretrained_model.parameters())
-part_parameters = list(net.proposal_net.parameters())
-concat_parameters = list(net.concat_net.parameters())
-partcls_parameters = list(net.partcls_net.parameters())
-
-raw_optimizer = torch.optim.SGD(raw_parameters, lr=LR, momentum=0.9, weight_decay=WD)
-concat_optimizer = torch.optim.SGD(concat_parameters, lr=LR, momentum=0.9, weight_decay=WD)
-part_optimizer = torch.optim.SGD(part_parameters, lr=LR, momentum=0.9, weight_decay=WD)
-partcls_optimizer = torch.optim.SGD(partcls_parameters, lr=LR, momentum=0.9, weight_decay=WD)
-
-...
-
-for i, data in enumerate(trainloader):
-    img, label = data[0].cuda(), data[1].cuda()
-    batch_size = img.size(0)
-    raw_optimizer.zero_grad()
-    part_optimizer.zero_grad()
-    concat_optimizer.zero_grad()
-    partcls_optimizer.zero_grad()
-    _, _, raw_logits, concat_logits, part_logits, _, top_n_prob = net(img)
-    part_loss = net.list_loss(part_logits.view(batch_size * PROPOSAL_NUM, -1),
-                                    label.unsqueeze(1).repeat(1, PROPOSAL_NUM).view(-1)).view(batch_size, PROPOSAL_NUM)
-    raw_loss = creterion(raw_logits, label)
-    concat_loss = creterion(concat_logits, label)
-    rank_loss = net.ranking_loss(top_n_prob, part_loss)
-    partcls_loss = creterion(part_logits.view(batch_size * PROPOSAL_NUM, -1),
-                                 label.unsqueeze(1).repeat(1, PROPOSAL_NUM).view(-1))
-
-    total_loss = raw_loss + rank_loss + concat_loss + partcls_loss
-    total_loss.backward()
-    raw_optimizer.step()
-    part_optimizer.step()
-    concat_optimizer.step()
-    partcls_optimizer.step()
-```
-
-
 ### Model Description
-This is a nts-net pretrained with CUB200 2011 dataset. A fine graned dataset of birds species. 
+This is an nts-net pretrained with CUB200 2011 dataset, which is a fine grained dataset of birds species. 
 
 ### References
 You can read the full paper at this [link](http://artelab.dista.uninsubria.it/res/research/papers/2019/2019-IVCNZ-Nawaz-Birds.pdf).