Merge pull request isl-org#190 from isl-org/thias15/update-links

update links

Dockerfile

@@ -22,7 +22,7 @@ COPY ./midas ./midas
 COPY ./*.py ./
 
 # download model weights so the docker image can be used offline
-RUN cd weights && {curl -OL https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/dpt_hybrid-midas-501f0c75.pt; cd -; }
+RUN cd weights && {curl -OL https://github.com/isl-org/MiDaS/releases/download/v3/dpt_hybrid-midas-501f0c75.pt; cd -; }
 RUN python3 run.py --model_type dpt_hybrid; exit 0
 
 # entrypoint (dont forget to mount input and output directories)

README.md

@@ -14,31 +14,31 @@ and our [preprint](https://arxiv.org/abs/2103.13413):
 
 MiDaS was trained on 10 datasets (ReDWeb, DIML, Movies, MegaDepth, WSVD, TartanAir, HRWSI, ApolloScape, BlendedMVS, IRS) with
 multi-objective optimization. 
-The original model that was trained on 5 datasets  (`MIX 5` in the paper) can be found [here](https://github.com/intel-isl/MiDaS/releases/tag/v2).
+The original model that was trained on 5 datasets  (`MIX 5` in the paper) can be found [here](https://github.com/isl-org/MiDaS/releases/tag/v2).
 
 
 ### Changelog
 * [Sep 2021] Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/DPT-Large).
 * [Apr 2021] Released MiDaS v3.0:
     - New models based on [Dense Prediction Transformers](https://arxiv.org/abs/2103.13413) are on average [21% more accurate](#Accuracy) than MiDaS v2.1
-    - Additional models can be found [here](https://github.com/intel-isl/DPT)
+    - Additional models can be found [here](https://github.com/isl-org/DPT)
 * [Nov 2020] Released MiDaS v2.1:
-	- New model that was trained on 10 datasets and is on average about [10% more accurate](#Accuracy) than [MiDaS v2.0](https://github.com/intel-isl/MiDaS/releases/tag/v2)
-	- New light-weight model that achieves [real-time performance](https://github.com/intel-isl/MiDaS/tree/master/mobile) on mobile platforms.
-	- Sample applications for [iOS](https://github.com/intel-isl/MiDaS/tree/master/mobile/ios) and [Android](https://github.com/intel-isl/MiDaS/tree/master/mobile/android)
-	- [ROS package](https://github.com/intel-isl/MiDaS/tree/master/ros) for easy deployment on robots
+	- New model that was trained on 10 datasets and is on average about [10% more accurate](#Accuracy) than [MiDaS v2.0](https://github.com/isl-org/MiDaS/releases/tag/v2)
+	- New light-weight model that achieves [real-time performance](https://github.com/isl-org/MiDaS/tree/master/mobile) on mobile platforms.
+	- Sample applications for [iOS](https://github.com/isl-org/MiDaS/tree/master/mobile/ios) and [Android](https://github.com/isl-org/MiDaS/tree/master/mobile/android)
+	- [ROS package](https://github.com/isl-org/MiDaS/tree/master/ros) for easy deployment on robots
 * [Jul 2020] Added TensorFlow and ONNX code. Added [online demo](http://35.202.76.57/).
 * [Dec 2019] Released new version of MiDaS - the new model is significantly more accurate and robust
-* [Jul 2019] Initial release of MiDaS ([Link](https://github.com/intel-isl/MiDaS/releases/tag/v1))
+* [Jul 2019] Initial release of MiDaS ([Link](https://github.com/isl-org/MiDaS/releases/tag/v1))
 
 ### Setup 
 
 1) Pick one or more models and download corresponding weights to the `weights` folder:
 
-- For highest quality: [dpt_large](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt)
-- For moderately less quality, but better speed on CPU and slower GPUs: [dpt_hybrid](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt)
-- For real-time applications on resource-constrained devices: [midas_v21_small](https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt)
-- Legacy convolutional model: [midas_v21](https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt)
+- For highest quality: [dpt_large](https://github.com/isl-org/MiDaS/releases/download/v3/dpt_large-midas-2f21e586.pt)
+- For moderately less quality, but better speed on CPU and slower GPUs: [dpt_hybrid](https://github.com/isl-org/MiDaS/releases/download/v3/dpt_hybrid-midas-501f0c75.pt)
+- For real-time applications on resource-constrained devices: [midas_v21_small](https://github.com/isl-org/MiDaS/releases/download/v2_1/midas_v21_small-70d6b9c8.pt)
+- Legacy convolutional model: [midas_v21](https://github.com/isl-org/MiDaS/releases/download/v2_1/midas_v21-f6b98070.pt)
 
 2) Set up dependencies: 
 
@@ -92,18 +92,18 @@ The pretrained model is also available on [PyTorch Hub](https://pytorch.org/hub/
 
 #### via TensorFlow or ONNX
 
-See [README](https://github.com/intel-isl/MiDaS/tree/master/tf) in the `tf` subdirectory.
+See [README](https://github.com/isl-org/MiDaS/tree/master/tf) in the `tf` subdirectory.
 
 Currently only supports MiDaS v2.1. DPT-based models to be added. 
 
 
 #### via Mobile (iOS / Android)
 
-See [README](https://github.com/intel-isl/MiDaS/tree/master/mobile) in the `mobile` subdirectory.
+See [README](https://github.com/isl-org/MiDaS/tree/master/mobile) in the `mobile` subdirectory.
 
 #### via ROS1 (Robot Operating System)
 
-See [README](https://github.com/intel-isl/MiDaS/tree/master/ros) in the `ros` subdirectory.
+See [README](https://github.com/isl-org/MiDaS/tree/master/ros) in the `ros` subdirectory.
 
 Currently only supports MiDaS v2.1. DPT-based models to be added. 
 
@@ -119,10 +119,10 @@ Zero-shot error (the lower - the better) and speed (FPS):
 | MiDaS v2.1 small [URL]() | 0.1344 | **0.1344** | 0.3370 | 29.27 | **13.43** | **14.53** | 30 |
 | | | | | | | |
 | **Big models:** | | | | | | | GPU RTX 3090 |
-| MiDaS v2 large [URL](https://github.com/intel-isl/MiDaS/releases/download/v2/model-f46da743.pt) | 0.1246 | 0.1290 | 0.3270 | 23.90 | 9.55 | 14.29 | 51 |
-| MiDaS v2.1 large [URL](https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt) | 0.1295 | 0.1155 | 0.3285 | 16.08 | 8.71 | 12.51 | 51 |
-| MiDaS v3.0 DPT-Hybrid [URL](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt) | 0.1106 | 0.0934 | 0.2741 | 11.56 | 8.69 | 10.89 | 46 |
-| MiDaS v3.0 DPT-Large [URL](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt) | **0.1082** | **0.0888** | **0.2697** | **8.46** | **8.32** | **9.97** | 47 |
+| MiDaS v2 large [URL](https://github.com/isl-org/MiDaS/releases/download/v2/model-f46da743.pt) | 0.1246 | 0.1290 | 0.3270 | 23.90 | 9.55 | 14.29 | 51 |
+| MiDaS v2.1 large [URL](https://github.com/isl-org/MiDaS/releases/download/v2_1/midas_v21-f6b98070.pt) | 0.1295 | 0.1155 | 0.3285 | 16.08 | 8.71 | 12.51 | 51 |
+| MiDaS v3.0 DPT-Hybrid [URL](https://github.com/isl-org/MiDaS/releases/download/v3/dpt_hybrid-midas-501f0c75.pt) | 0.1106 | 0.0934 | 0.2741 | 11.56 | 8.69 | 10.89 | 46 |
+| MiDaS v3.0 DPT-Large [URL](https://github.com/isl-org/MiDaS/releases/download/v3/dpt_large-midas-2f21e586.pt) | **0.1082** | **0.0888** | **0.2697** | **8.46** | **8.32** | **9.97** | 47 |
 
 
 

hubconf.py

@@ -20,7 +20,7 @@ def DPT_Large(pretrained=True, **kwargs):
 
     if pretrained:
         checkpoint = (
-            "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt"
+            "https://github.com/isl-org/MiDaS/releases/download/v3/dpt_large-midas-2f21e586.pt"
         )
         state_dict = torch.hub.load_state_dict_from_url(
             checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True
@@ -43,7 +43,7 @@ def DPT_Hybrid(pretrained=True, **kwargs):
 
     if pretrained:
         checkpoint = (
-            "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt"
+            "https://github.com/isl-org/MiDaS/releases/download/v3/dpt_hybrid-midas-501f0c75.pt"
         )
         state_dict = torch.hub.load_state_dict_from_url(
             checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True
@@ -62,7 +62,7 @@ def MiDaS(pretrained=True, **kwargs):
 
     if pretrained:
         checkpoint = (
-            "https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-f6b98070.pt"
+            "https://github.com/isl-org/MiDaS/releases/download/v2_1/model-f6b98070.pt"
         )
         state_dict = torch.hub.load_state_dict_from_url(
             checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True
@@ -81,7 +81,7 @@ def MiDaS_small(pretrained=True, **kwargs):
 
     if pretrained:
         checkpoint = (
-            "https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-small-70d6b9c8.pt"
+            "https://github.com/isl-org/MiDaS/releases/download/v2_1/model-small-70d6b9c8.pt"
         )
         state_dict = torch.hub.load_state_dict_from_url(
             checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True

mobile/android/README.md

@@ -18,4 +18,4 @@ To use another model, you should convert it to `model_opt.tflite` and place it t
 
 ----
 
-Original repository: https://github.com/intel-isl/MiDaS
+Original repository: https://github.com/isl-org/MiDaS

mobile/android/models/download.gradle

@@ -1,4 +1,4 @@
-def modelFloatDownloadUrl = "https://github.com/intel-isl/MiDaS/releases/download/v2_1/model_opt.tflite"
+def modelFloatDownloadUrl = "https://github.com/isl-org/MiDaS/releases/download/v2_1/model_opt.tflite"
 def modelFloatFile = "model_opt.tflite"
 
 task downloadModelFloat(type: Download) {

mobile/ios/README.md

@@ -33,7 +33,7 @@ pip install tensorflow
 
 ### Install TensorFlowLiteSwift via Cocoapods
 
-Set required TensorFlowLiteSwift version in the file (`0.0.1-nightly` is recommended): https://github.com/AlexeyAB/midas_tf_ios/blob/main/Podfile#L9
+Set required TensorFlowLiteSwift version in the file (`0.0.1-nightly` is recommended): https://github.com/isl-org/MiDaS/blob/master/mobile/ios/Podfile#L9
 
 Install: brew, ruby, cocoapods
 
@@ -82,7 +82,7 @@ open(model_tflite_name, "wb").write("model.tflite")
 
 ----
 
-Original repository: https://github.com/intel-isl/MiDaS
+Original repository: https://github.com/isl-org/MiDaS
 
 
 ### Examples:

mobile/ios/RunScripts/download_models.sh

@@ -3,7 +3,7 @@
 
 TFLITE_MODEL="model_opt.tflite"
 TFLITE_FILE="Midas/Model/${TFLITE_MODEL}"
-MODEL_SRC="https://github.com/intel-isl/MiDaS/releases/download/v2/${TFLITE_MODEL}"
+MODEL_SRC="https://github.com/isl-org/MiDaS/releases/download/v2/${TFLITE_MODEL}"
 
 if test -f "${TFLITE_FILE}"; then
     echo "INFO: TF Lite model already exists. Skip downloading and use the local model."

ros/README.md

@@ -18,14 +18,14 @@ MiDaS is a neural network to compute depth from a single image.
 
 * install ROS Melodic for Ubuntu 17.10 / 18.04:
 ```bash
-wget https://raw.githubusercontent.com/intel-isl/MiDaS/master/ros/additions/install_ros_melodic_ubuntu_17_18.sh
+wget https://raw.githubusercontent.com/isl-org/MiDaS/master/ros/additions/install_ros_melodic_ubuntu_17_18.sh
 ./install_ros_melodic_ubuntu_17_18.sh
 ```
 
 or Noetic for Ubuntu 20.04: 
 
 ```bash
-wget https://raw.githubusercontent.com/intel-isl/MiDaS/master/ros/additions/install_ros_noetic_ubuntu_20.sh
+wget https://raw.githubusercontent.com/isl-org/MiDaS/master/ros/additions/install_ros_noetic_ubuntu_20.sh
 ./install_ros_noetic_ubuntu_20.sh
 ```
 
@@ -61,7 +61,7 @@ source ~/.bashrc
 cd ~/
 mkdir catkin_ws
 cd catkin_ws
-git clone https://github.com/intel-isl/MiDaS
+git clone https://github.com/isl-org/MiDaS
 mkdir src
 cp -r MiDaS/ros/* src
 

ros/additions/downloads.sh

@@ -1,5 +1,5 @@
 mkdir ~/.ros
-wget https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-small-traced.pt
+wget https://github.com/isl-org/MiDaS/releases/download/v2_1/model-small-traced.pt
 cp ./model-small-traced.pt ~/.ros/model-small-traced.pt
 
 

ros/midas_cpp/package.xml

@@ -6,7 +6,7 @@
 
   <maintainer email="[email protected]">Alexey Bochkovskiy</maintainer>
   <license>MIT</license>
-  <url type="website">https://github.com/AlexeyAB/midas_ros</url>
+  <url type="website">https://github.com/isl-org/MiDaS/tree/master/ros</url>
   <!-- <author email="[email protected]">Alexey Bochkovskiy</author> -->
 
 

tf/README.md

@@ -11,8 +11,8 @@
 
 ### Run inference on TensorFlow-model by using TensorFlow
 
-1) Download the model weights [model-f6b98070.pb](https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-f6b98070.pb) 
-and [model-small.pb](https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-small.pb) and place the
+1) Download the model weights [model-f6b98070.pb](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-f6b98070.pb) 
+and [model-small.pb](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-small.pb) and place the
 file in the `/tf/` folder.
 
 2) Set up dependencies: 
@@ -47,8 +47,8 @@ pip install -I grpcio tensorflow==2.3.0 tensorflow-addons==0.11.2 numpy==1.18.0
 
 ### Run inference on ONNX-model by using ONNX-Runtime
 
-1) Download the model weights [model-f6b98070.onnx](https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-f6b98070.onnx) 
-and [model-small.onnx](https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-small.onnx) and place the
+1) Download the model weights [model-f6b98070.onnx](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-f6b98070.onnx) 
+and [model-small.onnx](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-small.onnx) and place the
 file in the `/tf/` folder.
 
 2) Set up dependencies: 
@@ -87,7 +87,7 @@ pip install onnxruntime==1.5.2
 
 ### Make ONNX model from downloaded Pytorch model file
 
-1) Download the model weights [model-f6b98070.pt](https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-f6b98070.pt) and place the
+1) Download the model weights [model-f6b98070.pt](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-f6b98070.pt) and place the
 file in the root folder.
 
 2) Set up dependencies: