Update README

README.md

@@ -8,20 +8,43 @@
 This implements training of [Residual Attention Network](https://arxiv.org/abs/1704.06904) on the ImageNet dataset, and provide the pretrained weights.
 
 
+# Install 
+
+```bash
+pip install 'git+ssh://git@github.com/phamquiluan/ResidualAttentionNetwork.git@v0.0.1'
+```
+
+# Quickstart
+
+```python
+import torch
+from resattnet import resattnet56
+
+m = resattnet56(in_channels=3, num_classes=10)  # pretrained is load automatically
+
+tensor = torch.Tensor(1, 3, 224, 224)
+
+output = m(tensor)
+
+print(output.shape)  # torch.Size([1, 10])
+```
+
 ## Pretrained Download
 
 Download resattnet56 pretrained Imagenet1K: [link](https://drive.google.com/file/d/1Sc-TCERxrJKN4TvmDOwn_98GeUva_FIr/view?usp=sharing)
 
 Eval: Acc@1 77.024 Acc@5 93.574
 
 
-## Requirements
+
+
+## Training
 
 - Install PyTorch ([pytorch.org](http://pytorch.org))
 - `pip install -r requirements.txt`
 - Download the ImageNet dataset from http://www.image-net.org/
-    - Then, and move validation images to labeled subfolders, using [the following shell script](https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh)
-## Training
+- Then, and move validation images to labeled subfolders, using [the following shell script](https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh)
+
 
 To train a model, run `main.py` with the desired model architecture and the path to the ImageNet dataset: