Update README.md

README.md

@@ -33,7 +33,7 @@ Approximate [FPN](https://arxiv.org/abs/1612.03144) *baseline* [setup](https://g
   - For COCO, we find the performance improving with more iterations (VGG16 350k/490k: 26.9, 600k/790k: 28.3, 900k/1190k: 29.5), and potentially better performance can be achieved with even more iterations. 
   - For Resnets, we fix the first block (total 4) when fine-tuning the network, and only use ``crop_and_resize`` to resize the RoIs (7x7) without max-pool (which I find useless especially for COCO). The final feature maps are average-pooled for classification and regression. All batch normalization parameters are fixed. Weight decay is set to Renset101 default 1e-4. Learning rate for biases is not doubled.
   - For approximate [FPN](https://arxiv.org/abs/1612.03144) baseline setup we simply resize the image with 800 pixels, add 32^2 anchors, and take 1000 proposals during testing.
-  - Check out [here](http://ladoga.graphics.cs.cmu.edu/xinleic/tf-faster-rcnn/)/[here](http://gs11655.sp.cs.cmu.edu/xinleic/tf-faster-rcnn/)/[here](https://drive.google.com/open?id=0B1_fAEgxdnvJSmF3YUlZcHFqWTQ) for the latest models, including longer COCO VGG16 models and Resnet101 ones.
+  - Check out [here](http://ladoga.graphics.cs.cmu.edu/xinleic/tf-faster-rcnn/)/[here](http://gs11655.sp.cs.cmu.edu/xinleic/tf-faster-rcnn/)/[here](https://drive.google.com/open?id=0B1_fAEgxdnvJSmF3YUlZcHFqWTQ) for the latest models, including longer COCO VGG16 models and Resnet ones.
 
 ### Additional features
 Additional features not mentioned in the [report](https://arxiv.org/pdf/1702.02138.pdf) are added to make research life easier:
@@ -184,7 +184,7 @@ tensorboard/[NET]/[DATASET]/default/
 tensorboard/[NET]/[DATASET]/default_val/
 ```
 
-The default number of training iterations is kept the same to the original faster RCNN for VOC 2007, however I find it is beneficial to train longer (see [report](https://arxiv.org/pdf/1702.02138.pdf) for COCO), probably due to the fact that the image batch size is 1. For VOC 07+12 we switch to a 80k/110k schedule following [R-FCN](https://github.com/daijifeng001/R-FCN). Also note that due to the nondeterministic nature of the current implementation, the performance can vary a bit, but in general it should be within ~1% of the reported numbers for VOC, and ~0.2% of the reported numbers for COCO. Suggestions/Contributions are welcome.
+The default number of training iterations is kept the same to the original faster RCNN for VOC 2007, however I find it is beneficial to train longer (see [report](https://arxiv.org/pdf/1702.02138.pdf) for COCO), probably due to the fact that the image batch size is one. For VOC 07+12 we switch to a 80k/110k schedule following [R-FCN](https://github.com/daijifeng001/R-FCN). Also note that due to the nondeterministic nature of the current implementation, the performance can vary a bit, but in general it should be within ~1% of the reported numbers for VOC, and ~0.2% of the reported numbers for COCO. Suggestions/Contributions are welcome.
 
 ### Citation
 If you find this implementation or the analysis conducted in our report helpful, please consider citing: