Fixes arXiv link (#1)

- Fixes arXiv link (d3f604e61eb5af68c9f1e0cdb1f48d7115479ee1)
- fixed other arXiv link (23a440efa12e68faf799c948b34692e3c9ba3b11)


Co-authored-by: Hilmar <hlapp@users.noreply.huggingface.co>

README.md

@@ -19,8 +19,8 @@ This model takes in an image of a fish and segments out traits, as described [be
 See [github.com/Cadene/pretrained-models.pytorch#resnext](https://github.com/Cadene/pretrained-models.pytorch#resnext) for documentation about the source.
 
 The segmentation model was first trained on ImageNet ([Deng et al., 2009](https://doi.org/10.1109/CVPR.2009.5206848)), and then the model was fine-tuned on a specific set of image data relevant to the domain: [Illinois Natural History Survey Fish Collection](https://fish.inhs.illinois.edu/) (INHS Fish). 
-The Feature Pyramid Network (FPN) architecture was used for fine-tuning, since it is a CNN-based architecture designed to handle multi-scale feature maps (Lin et al., 2017: [IEEE](https://doi.org/10.1109/CVPR.2017.106), [arXiv](arXiv:1612.03144)). 
-The FPN uses SE-ResNeXt as the base network (Hu et al., 2018: [IEEE](https://doi.org/10.1109/CVPR.2018.00745), [arXiv](arXiv:1709.01507)).
+The Feature Pyramid Network (FPN) architecture was used for fine-tuning, since it is a CNN-based architecture designed to handle multi-scale feature maps (Lin et al., 2017: [IEEE](https://doi.org/10.1109/CVPR.2017.106), [arXiv](https://doi.org/10.48550/arXiv.1612.03144)). 
+The FPN uses SE-ResNeXt as the base network (Hu et al., 2018: [IEEE](https://doi.org/10.1109/CVPR.2018.00745), [arXiv](https://arxiv.org/abs/1709.01507)).
 
 
 ### Model Description