feat(models): add Faster-RCNN md file (#6)

models/megengine_vision_faster_rcnn.md

+---
+template: hub1
+title: Faster-RCNN
+summary:
+    en_US: Faster-RCNN pre-trained on COCO
+    zh_CN: Faster-RCNN (COCO预训练权重）
+author: MegEngine Team
+tags: [vision, detection]
+github-link: https://github.com/megengine/models
+---
+
+```python
+from megengine import hub
+model = hub.load(
+    "megengine/models",
+    "faster_rcnn_fpn_res50_coco_1x_800size",
+    pretrained=True,
+    use_cache=False,
+)
+model.eval()
+
+models_api = hub.import_module(
+    "megengine/models",
+    git_host="github.com",
+)
+```
+<!-- section: zh_CN --> 
+
+所有预训练模型希望数据被正确预处理。
+模型要求输入BGR的图片, 同时需要等比例缩放到：短边和长边分别不超过800/1333
+最后做归一化处理 (均值为: `[103.530, 116.280, 123.675]`, 标准差为: `[57.375, 57.120, 58.395]`)。
+
+下面是一段处理一张图片的样例代码。
+
+```python
+# Download an example image from the megengine data website
+import urllib
+url, filename = ("https://data.megengine.org.cn/images/cat.jpg", "cat.jpg")
+try: urllib.URLopener().retrieve(url, filename)
+except: urllib.request.urlretrieve(url, filename)
+
+# Read and pre-process the image
+import cv2
+image = cv2.imread("cat.jpg")
+
+data, im_info = models_api.DetEvaluator.process_inputs(image, 800, 1333)
+model.inputs["image"].set_value(data)
+model.inputs["im_info"].set_value(im_info)
+
+from megengine import jit
+@jit.trace(symbolic=True)
+def infer():
+    predictions = model(model.inputs)
+    return predictions
+    
+print(infer())
+```
+
+### 模型描述
+
+目前我们提供了retinanet的预训练模型, 在coco验证集上的结果如下：
+
+| model                        | mAP<br>@5-95     |  
+| ---                          |     ---          |
+| faster-rcnn-res50-1x-800size |    37.3          |
+
+### 参考文献
+
+- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/pdf/1506.01497.pdf) S. Ren, K. He, R. Girshick, and J. Sun. In: Neural Information Processing Systems(NIPS)(2015).
+- [Feature Pyramid Networks for Object Detection](https://arxiv.org/pdf/1612.03144.pdf) T. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan and S. Belongie. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 936-944, doi: 10.1109/CVPR.2017.106.
+- [Microsoft COCO: Common Objects in Context](https://arxiv.org/pdf/1405.0312.pdf)  Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Dollár, Piotr and Zitnick, C Lawrence, Lin T Y, Maire M, Belongie S, et al. European conference on computer vision. Springer, Cham, 2014: 740-755.
+
+ 
+<!-- section: en_US --> 
+
+All pre-trained models expect input images normalized in the same way,
+i.e. input images must be 3-channel BGR images of shape `(H x W x 3)`, and reszied shortedge/longedge to no more than `800/1333`.
+The images should be normalized using `mean = [103.530, 116.280, 123.675]` and `std = [57.375, 57.120, 58.395])`.
+
+Here's a sample execution.
+
+```python
+# Download an example image from the megengine data website
+import urllib
+url, filename = ("https://data.megengine.org.cn/images/cat.jpg", "cat.jpg")
+try: urllib.URLopener().retrieve(url, filename)
+except: urllib.request.urlretrieve(url, filename)
+
+# Read and pre-process the image
+import cv2
+image = cv2.imread("cat.jpg")
+
+data, im_info = models_api.DetEvaluator.process_inputs(image, 800, 1333)
+model.inputs["image"].set_value(data)
+model.inputs["im_info"].set_value(im_info)
+
+from megengine import jit
+@jit.trace(symbolic=True)
+def infer():
+    predictions = model(model.inputs)
+    return predictions
+    
+print(infer())
+```
+
+### Model Description
+
+Currently we provide a `retinanet` model which is pretrained on `COCO2017` training set. The mAP on `COCO2017` val set can be found in following table.
+
+| model                        | mAP<br>@5-95     |  
+| ---                          |     ---          |
+| faster-rcnn-res50-1x-800size |    37.3          |
+
+### References
+
+- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/pdf/1506.01497.pdf) S. Ren, K. He, R. Girshick, and J. Sun. In: Neural Information Processing Systems(NIPS)(2015).
+- [Feature Pyramid Networks for Object Detection](https://arxiv.org/pdf/1612.03144.pdf) T. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan and S. Belongie. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 936-944, doi: 10.1109/CVPR.2017.106.
+- [Microsoft COCO: Common Objects in Context](https://arxiv.org/pdf/1405.0312.pdf)  Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Dollár, Piotr and Zitnick, C Lawrence, Lin T Y, Maire M, Belongie S, et al. European conference on computer vision. Springer, Cham, 2014: 740-755.

models/megengine_vision_retinanet.md

@@ -13,8 +13,9 @@ github-link: https://github.com/megengine/models
 from megengine import hub
 model = hub.load(
     "megengine/models",
-    "retinanet_res50_1x_800size",
+    "retinanet_res50_coco_1x_800size",
     pretrained=True,
+    use_cache=False,
 )
 model.eval()