Update docker image version (#2)

* Create requirements.txt * Add demo usage instructions to app_options.yaml * Update screenshots in README * Update requirements.txt * Update Dockerfile and configuration for RT-DETRv2 to version 1.0.3; remove requirements.txt
supervisely-ecosystem · Dec 13, 2024 · ab1bd1e · ab1bd1e
1 parent f658902
commit ab1bd1e
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Showing 6 changed files with 20 additions and 13 deletions.
diff --git a/docker/Dockerfile b/docker/Dockerfile
@@ -31,7 +31,7 @@ RUN pip install --no-cache-dir \
     tensorboard \
     imgaug==0.4.0
 
-RUN pip install --no-cache-dir supervisely[training]==6.73.251
+RUN pip install --no-cache-dir supervisely[training]==6.73.255
 
 RUN mkdir -p ~/.cache/supervisely/checkpoints
 RUN curl -L -o ~/.cache/supervisely/checkpoints/rtdetrv2_r18vd_120e_coco_rerun_48.1.pth https://github.com/lyuwenyu/storage/releases/download/v0.2/rtdetrv2_r18vd_120e_coco_rerun_48.1.pth

diff --git a/docker/publish.sh b/docker/publish.sh
@@ -1,2 +1,2 @@
-docker build -t supervisely/rt-detrv2:1.0.2 . && \
-docker push supervisely/rt-detrv2:1.0.2
+docker build -t supervisely/rt-detrv2:1.0.3 . && \
+docker push supervisely/rt-detrv2:1.0.3
diff --git a/supervisely_integration/serve/config.json b/supervisely_integration/serve/config.json
@@ -13,7 +13,7 @@
 	"gpu": "preferred",
 	"session_tags": ["deployed_nn"],
 	"community_agent": false,
-	"docker_image": "supervisely/rt-detrv2:1.0.2",
+	"docker_image": "supervisely/rt-detrv2:1.0.3",
 	"task_location": "application_sessions",
 	"license": {
 		"type": "Apache-2.0"

diff --git a/supervisely_integration/train/README.md b/supervisely_integration/train/README.md
@@ -37,39 +37,39 @@ Train RT-DETRv2 models in Supervisely on your custom data. RT-DETRv2 (Real-Time
 
 **Step 1.** Select if you want to use cached project or redownload it
 
-<img src="https://github.com/user-attachments/assets/5d992899-e82f-449a-8817-8a261f033403" width="100%" style='padding-top: 10px'>  
+<img src="https://github.com/user-attachments/assets/8c2b5f4b-1abd-44f5-8f0e-d2890284d8ba" width="100%" style='padding-top: 10px'>  
 
 **Step 2.** Select train / val split
 
-<img src="https://github.com/user-attachments/assets/71a682a6-b59a-4338-9697-b37d735a5f58" width="100%" style='padding-top: 10px'>  
+<img src="https://github.com/user-attachments/assets/aec211b8-6eee-46a8-a70f-04bbf6ed4fa5" width="100%" style='padding-top: 10px'>  
 
 **Step 3.** Select the classes you want to train RT-DETRv2 on
 
-<img src="https://github.com/user-attachments/assets/0546e425-453e-4e1d-b47d-892e68da04e5" width="100%" style='padding-top: 10px'>  
+<img src="https://github.com/user-attachments/assets/55068763-268a-437e-8492-0368fba4fb13" width="100%" style='padding-top: 10px'>  
 
 **Step 4.** Select the model you want to train
 
-<img src="https://github.com/user-attachments/assets/d58dbe20-ba53-4525-9407-c037fff90655" width="100%" style='padding-top: 10px'>  
+<img src="https://github.com/user-attachments/assets/753f6f0f-a38f-4d61-accb-dc8ba41777c0" width="100%" style='padding-top: 10px'>  
 
 **Step 5.** Configure hyperaparameters and select whether you want to use model evaluation and convert checkpoints to ONNX and TensorRT
 
-<img src="https://github.com/user-attachments/assets/d82161c2-2ba5-42af-b651-e4bfc55aad3a" width="100%" style='padding-top: 10px'>  
+<img src="https://github.com/user-attachments/assets/59da4672-2f82-480f-b2c5-fb118878ff29" width="100%" style='padding-top: 10px'>  
 
 **Step 6.** Enter experiment name and start training
 
-<img src="https://github.com/user-attachments/assets/6eac9d70-bfd9-4105-949e-2394a13395a2" width="100%" style='padding-top: 10px'>  
+<img src="https://github.com/user-attachments/assets/252b085f-4597-494e-afaa-ae0a19f0e7ba" width="100%" style='padding-top: 10px'>  
 
 **Step 7.** Monitor training progress
 
-<img src="https://github.com/user-attachments/assets/d82216f6-0983-4717-8140-69b589d1b0fa" width="100%" style='padding-top: 10px'>  
+<img src="https://github.com/user-attachments/assets/0b157c66-b3a0-4bd2-ba93-c3c8e8ff72bb" width="100%" style='padding-top: 10px'>  
 
 # Obtain saved checkpoints
 
 All the trained checkpoints, that are generated through the process of training models are stored in [Team Files](https://app.supervise.ly/files/) in the **experiments** folder.
 
 You will see a folder thumbnail with a link to your saved checkpoints by the end of training process.
 
-<img src="https://github.com/user-attachments/assets/31aab070-343b-4890-ac4b-0f1ca7efcee2" width="100%" style='padding-top: 10px'>  
+<img src="https://github.com/user-attachments/assets/c075107c-6493-401e-8276-14cdf6f61be6" width="100%" style='padding-top: 10px'>  
 
 # Acknowledgment
 

diff --git a/supervisely_integration/train/app_options.yaml b/supervisely_integration/train/app_options.yaml
@@ -8,6 +8,13 @@ model_benchmark: true
 export_onnx_supported: true
 export_tensorrt_supported: true
 
+# How to use models outside of the Supervisely platform
+demo:
+  overview: "https://github.com/supervisely-ecosystem/RT-DETRv2/blob/main/supervisely_integration/demo/README.md"
+  pytorch: "https://github.com/supervisely-ecosystem/RT-DETRv2/blob/main/supervisely_integration/demo/demo_pytorch.py"
+  onnx: "https://github.com/supervisely-ecosystem/RT-DETRv2/blob/main/supervisely_integration/demo/demo_onnx.py"
+  tensorrt: "https://github.com/supervisely-ecosystem/RT-DETRv2/blob/main/supervisely_integration/demo/demo_tensorrt.py"
+
 # Enable this option when using supervisely train logger
 # train_logger: "tensorboard"
 

diff --git a/supervisely_integration/train/config.json b/supervisely_integration/train/config.json
@@ -9,7 +9,7 @@
 	"icon_cover": true,
 	"poster": "https://github.com/user-attachments/assets/b4554de5-3d2c-4b4f-aba9-95864cb05289",
 	"description": "Train RT-DETRv2 model on your data",
-	"docker_image": "supervisely/rt-detrv2:1.0.2",
+	"docker_image": "supervisely/rt-detrv2:1.0.3",
 	"instance_version": "6.12.12",
 	"task_location": "workspace_tasks",
 	"need_gpu": true,