Merge pull request #537 from microsoft/PreRelease

add release note link to readme

README.md

@@ -41,11 +41,12 @@ For those interested in accessing the previous MegaDetector repository, which ut
 ## 📣 Announcements
 
 ### 🎉🎉🎉 Pytorch-Wildlife Version 1.1.0 is out!
-- MegaDetectorV6 is finally out! Please refer to our [next section](#racing_cardashdash-megadetectorv6-smaller-better-and-faster) and our [release page](https://github.com/microsoft/CameraTraps/releases) for more details! 
+- MegaDetectorV6 is finally out! Please refer to our [next section](#racing_cardashdash-megadetectorv6-smaller-better-and-faster) and our [release notes](https://github.com/microsoft/CameraTraps/releases/tag/pw_v1.1.0) for more details! 
 - We have incorporated a point-based overhead animal detection model into our model zoo called [HerdNet (Delplanque et al. 2022)](https://www.sciencedirect.com/science/article/pii/S092427162300031X?via%3Dihub). Two model weights are incorporated in this release, `HerdNet-general` (their default weights) and `HerdNet-ennedi` (their model trained on Ennedi 2019 datasets). More details can be found [here](PytorchWildlife/models/detection/herdnet/Herdnet.md) and in their original [repo](https://github.com/Alexandre-Delplanque/HerdNet). This is the first third-party model in Pytorch-Wildlife and the foundation of our expansion to overhead/aerial animal detection and classification. Please see our [HerdNet demo](demo/image_detection_demo_herdnet.ipynb) on how to use it!
 - You can now load custom weights you fine-tuned on your own datasets using the [finetuning module](PW_FT_classification) directly in the Pytorch-Wildlife pipeline! Please see the [demo](demo/custom_weight_loading_v6.ipynb) on how to do it. You can also load it in our Gradio app!
 - You can now automatically separate your image detections into folders based on detection results! Please see our [folder separation demo](demo/image_separation_demo_v6.ipynb) on how to do it. You can also test it in our Gradio demo!
-- We have also simplified the batch detection pipeline. Now we do not need to define pytorch datasets and dataloaders specifically. Please make sure to change your code and check our [release notes]() and our [new demo](demo/image_demo.py#58) for more details.
+- We have also simplified the batch detection pipeline. Now we do not need to define pytorch datasets and dataloaders specifically. Please make sure to change your code and check our [release notes](https://github.com/microsoft/CameraTraps/releases/tag/pw_v1.1.0) and our [new demo](demo/image_demo.py#58) for more details.
+
 
 <details>
 <summary><font size="3">👉 Click for more updates</font></summary>

megadetector.md

@@ -41,11 +41,11 @@ For those interested in accessing the previous MegaDetector repository, which ut
 ## 📣 Announcements
 
 ### 🎉🎉🎉 Pytorch-Wildlife Version 1.1.0 is out!
-- MegaDetectorV6 is finally out! Please refer to our [next section](#racing_cardashdash-megadetectorv6-smaller-better-and-faster) and our [release page](https://github.com/microsoft/CameraTraps/releases) for more details! 
+- MegaDetectorV6 is finally out! Please refer to our [next section](#racing_cardashdash-megadetectorv6-smaller-better-and-faster) and our [release notes](https://github.com/microsoft/CameraTraps/releases/tag/pw_v1.1.0) for more details! 
 - We have incorporated a point-based overhead animal detection model into our model zoo called [HerdNet (Delplanque et al. 2022)](https://www.sciencedirect.com/science/article/pii/S092427162300031X?via%3Dihub). Two model weights are incorporated in this release, `HerdNet-general` (their default weights) and `HerdNet-ennedi` (their model trained on Ennedi 2019 datasets). More details can be found [here](PytorchWildlife/models/detection/herdnet/Herdnet.md) and in their original [repo](https://github.com/Alexandre-Delplanque/HerdNet). This is the first third-party model in Pytorch-Wildlife and the foundation of our expansion to overhead/aerial animal detection and classification. Please see our [HerdNet demo](demo/image_detection_demo_herdnet.ipynb) on how to use it!
 - You can now load custom weights you fine-tuned on your own datasets using the [finetuning module](PW_FT_classification) directly in the Pytorch-Wildlife pipeline! Please see the [demo](demo/custom_weight_loading_v6.ipynb) on how to do it. You can also load it in our Gradio app!
 - You can now automatically separate your image detections into folders based on detection results! Please see our [folder separation demo](demo/image_separation_demo_v6.ipynb) on how to do it. You can also test it in our Gradio demo!
-- We have also simplified the batch detection pipeline. Now we do not need to define pytorch datasets and dataloaders specifically. Please make sure to change your code and check our [release notes]() and our [new demo](demo/image_demo.py#58) for more details.
+- We have also simplified the batch detection pipeline. Now we do not need to define pytorch datasets and dataloaders specifically. Please make sure to change your code and check our [release notes](https://github.com/microsoft/CameraTraps/releases/tag/pw_v1.1.0) and our [new demo](demo/image_demo.py#58) for more details.
 
 <details>
 <summary><font size="3">👉 Click for more updates</font></summary>