ssd_mobilenet_v1_coco_2017_11_17

{ssd_mobilenet_v2_coco_2018_03_29 → ssd_mobilenet_v1_coco_2017_11_17}/LICENSE

@@ -1,13 +1,4 @@
-Copyright 2022 Google LLC. All rights reserved.
-
-All files in the following folders:
-/community
-/official
-/orbit
-/research
-/tensorflow_models
-
-Are licensed as follows:
+Copyright 2015 The TensorFlow Authors.  All rights reserved.
 
                                  Apache License
                            Version 2.0, January 2004
@@ -197,7 +188,7 @@ Are licensed as follows:
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright 2016, The Authors.
+   Copyright 2015, The TensorFlow Authors.
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.

{ssd_mobilenet_v2_coco_2018_03_29 → ssd_mobilenet_v1_coco_2017_11_17}/README.md

@@ -1,21 +1,21 @@
-# SSD MobileNet v2 COCO
+# SSD MobileNet v1 COCO
 
-Object detection with a Single Shot MultiBox Detector (SSD) built on a MobileNet v2 backbone,
+Object detection with a Single Shot MultiBox Detector (SSD) built on a MobileNet v1 backbone,
 trained on the COCO dataset. The model was originally distributed as a frozen TensorFlow graph
-(`ssd_mobilenet_v2_coco_2018_03_29.pb`) and converted to ONNX for use with OpenCV's DNN module.
+(`ssd_mobilenet_v1_coco_2017_11_17.pb`) and converted to ONNX for use with OpenCV's DNN module.
 
 ## Model Details
-- **Architecture**: SSD (Single Shot MultiBox Detector) with MobileNet v2 backbone
+- **Architecture**: SSD (Single Shot MultiBox Detector) with MobileNet v1 backbone
 - **Input**: RGB image, 300×300, raw uint8, NHWC layout (`image_tensor:0`, shape `[1, 300, 300, 3]`)
 - **Output**: `detection_boxes:0` (normalized `ymin, xmin, ymax, xmax`), `detection_scores:0`, `detection_classes:0` (COCO class ids), `num_detections:0`
 - **Framework**: ONNX (converted from the TensorFlow frozen graph via tf2onnx, opset 18)
-- **Original weights**: http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz
+- **Original weights**: http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2017_11_17.tar.gz
 
 ## Usage
 
 ### Python
 ```bash
-python demo.py --model ssd_mobilenet_v2_coco_2018_03_29_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png --conf 0.3
+python demo.py --model ssd_mobilenet_v1_coco_2017_11_17_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png --conf 0.3
 ```
 
 ### C++
@@ -35,7 +35,7 @@ g++ -std=c++17 demo.cpp -o demo \
   -I$OCVBUILD \
   -L$ORT/lib -Wl,-rpath,$ORT/lib -lonnxruntime \
   -L$OCVBUILD/lib -Wl,-rpath,$OCVBUILD/lib -lopencv_imgcodecs -lopencv_imgproc -lopencv_core
-./demo --model ssd_mobilenet_v2_coco_2018_03_29_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png
+./demo --model ssd_mobilenet_v1_coco_2017_11_17_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png
 ```
 
 ## Conversion
@@ -45,7 +45,7 @@ via [convert_to_onnx.py](./convert_to_onnx.py) — input `image_tensor:0`, outpu
 Requires `tensorflow`, `tf2onnx`, and `onnx`.
 
 ```bash
-python convert_to_onnx.py --pb ../pb/ssd_mobilenet_v2_coco_2018_03_29.pb
+python convert_to_onnx.py --pb ../pb/ssd_mobilenet_v1_coco_2017_11_17.pb
 ```
 
 ## License

{ssd_mobilenet_v2_coco_2018_03_29 → ssd_mobilenet_v1_coco_2017_11_17}/convert_to_onnx.py

@@ -14,8 +14,8 @@ def load_graph_def(pb_path):
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Export ssd_mobilenet_v2_coco_2018_03_29.pb to ONNX")
-    parser.add_argument("--pb", default="../pb/ssd_mobilenet_v2_coco_2018_03_29.pb")
+    parser = argparse.ArgumentParser(description="Export ssd_mobilenet_v1_coco_2017_11_17.pb to ONNX")
+    parser.add_argument("--pb", default="../pb/ssd_mobilenet_v1_coco_2017_11_17.pb")
     parser.add_argument("--opset", type=int, default=18)
     args = parser.parse_args()
 
@@ -30,7 +30,7 @@ def main():
     onnx.checker.check_model(model_proto)
 
     stamp = datetime.datetime.now().strftime("%Y%b").lower()
-    onnx_path = "ssd_mobilenet_v2_coco_2018_03_29_%s.onnx" % stamp
+    onnx_path = "ssd_mobilenet_v1_coco_2017_11_17_%s.onnx" % stamp
     with open(onnx_path, "wb") as f:
         f.write(model_proto.SerializeToString())
     print("wrote", onnx_path)

{ssd_mobilenet_v2_coco_2018_03_29 → ssd_mobilenet_v1_coco_2017_11_17}/demo.cpp

@@ -18,7 +18,7 @@ static std::string argVal(int argc, char** argv, const std::string& key, const s
 
 int main(int argc, char** argv)
 {
-    std::string model  = argVal(argc, argv, "--model",  "ssd_mobilenet_v2_coco_2018_03_29_2026jul.onnx");
+    std::string model  = argVal(argc, argv, "--model",  "ssd_mobilenet_v1_coco_2017_11_17_2026jul.onnx");
     std::string image  = argVal(argc, argv, "--image",  "example_outputs/input_image.png");
     std::string output = argVal(argc, argv, "--output", "example_outputs/output_image.png");
     float conf = std::stof(argVal(argc, argv, "--conf", "0.3"));
@@ -92,7 +92,7 @@ int main(int argc, char** argv)
     }
 
     imwrite(output, out);
-    std::cout << "ssd_mobilenet_v2_coco_2018_03_29 " << lines.size() << " detections" << std::endl;
+    std::cout << "ssd_mobilenet_v1_coco_2017_11_17 " << lines.size() << " detections" << std::endl;
     for (const auto& l : lines) std::cout << l << std::endl;
     return 0;
 }

{ssd_mobilenet_v2_coco_2018_03_29 → ssd_mobilenet_v1_coco_2017_11_17}/demo.py

@@ -10,7 +10,7 @@ here = os.path.dirname(os.path.abspath(__file__))
 
 
 def main():
-    parser = argparse.ArgumentParser(description="SSD MobileNet v2 COCO (ONNX) object detection demo")
+    parser = argparse.ArgumentParser(description="SSD MobileNet v1 COCO (ONNX) object detection demo")
     parser.add_argument("--model", default=(glob.glob(os.path.join(here, "*.onnx")) or [""])[0])
     parser.add_argument("--image", default=os.path.join(here, "example_outputs", "input_image.png"))
     parser.add_argument("--output", default=os.path.join(here, "example_outputs", "output_image.png"))
@@ -43,7 +43,7 @@ def main():
         cv.putText(out, "%d:%.2f" % (int(classes[k]), scores[k]), (int(xmin * w), int(ymin * h) - 5), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
 
     cv.imwrite(args.output, out)
-    print("ssd_mobilenet_v2_coco_2018_03_29", len(kept), "detections")
+    print("ssd_mobilenet_v1_coco_2017_11_17", len(kept), "detections")
     for c, s, xmin, ymin, xmax, ymax in kept:
         print(c, round(s, 3), round(xmin, 3), round(ymin, 3), round(xmax, 3), round(ymax, 3))
 

ssd_mobilenet_v2_coco_2018_03_29/ssd_mobilenet_v2_coco_2018_03_29_2026jul.onnx → ssd_mobilenet_v1_coco_2017_11_17/ssd_mobilenet_v1_coco_2017_11_17_2026jul.onnx

@@ -1,3 +1,3 @@
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-oid sha256:7ba2fdaa87b8cbbb52c16b5c6e31a7452c00e8ad68aec580bfb7b07f5b212619
-size 69584537
+oid sha256:7423295086c7ceaca3ccc0059c16989456a08c3e4b8c05e2cc415240f9148e65
+size 29289721