tensorflow inception model

tensorflow_inception_graph/LICENSE

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tensorflow_inception_graph/README.md

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+# TensorFlow Inception
+
+Image classification with the Inception v1 (GoogLeNet) network trained on ImageNet.
+The model was originally distributed as a frozen TensorFlow graph
+(`tensorflow_inception_graph.pb`) and converted to ONNX for use with OpenCV's DNN module.
+
+## Model Details
+- **Architecture**: Inception v1 / GoogLeNet
+- **Input**: RGB image, 224×224, raw 0–255 float, NHWC layout (`input:0`, shape `[1, 224, 224, 3]`)
+- **Output**: ImageNet class scores, softmax over 1008 classes (`softmax2:0`)
+- **Framework**: ONNX (converted from the TensorFlow frozen graph via tf2onnx, opset 18)
+- **Original weights**: https://github.com/petewarden/tf_ios_makefile_example/raw/master/data/tensorflow_inception_graph.pb
+
+## Usage
+
+### Python
+```bash
+python demo.py --model tensorflow_inception_graph_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png
+```
+
+Or import directly:
+```python
+import cv2
+
+net = cv2.dnn.readNet("tensorflow_inception_graph_2026jul.onnx")
+# see demo.py for the full inference pipeline
+```
+
+### C++
+```bash
+cmake -B build && cmake --build build
+./build/demo --model tensorflow_inception_graph_2026jul.onnx --image example_outputs/input_image.png
+```
+
+## Conversion
+The ONNX model was exported from the frozen TensorFlow graph with tf2onnx (opset 18)
+via [convert_to_onnx.py](./convert_to_onnx.py) — inputs `input:0`, outputs `softmax2:0`,
+input shape overridden to `[1, 224, 224, 3]`. Requires `tensorflow`, `tf2onnx`, and `onnx`.
+
+```bash
+python convert_to_onnx.py --pb ../pb/tensorflow_inception_graph.pb
+```
+
+## License
+See [LICENSE](./LICENSE) — the model is released by the TensorFlow Authors under the Apache License 2.0.

tensorflow_inception_graph/convert_to_onnx.py

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+import argparse
+import datetime
+
+import onnx
+import tensorflow as tf
+import tf2onnx
+
+
+def load_graph_def(pb_path):
+    with tf.io.gfile.GFile(pb_path, "rb") as f:
+        graph_def = tf.compat.v1.GraphDef()
+        graph_def.ParseFromString(f.read())
+    return graph_def
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Export tensorflow_inception_graph.pb to ONNX")
+    parser.add_argument("--pb", default="../pb/tensorflow_inception_graph.pb")
+    parser.add_argument("--opset", type=int, default=18)
+    args = parser.parse_args()
+
+    graph_def = load_graph_def(args.pb)
+
+    model_proto, _ = tf2onnx.convert.from_graph_def(
+        graph_def,
+        input_names=["input:0"],
+        output_names=["softmax2:0"],
+        opset=args.opset,
+        shape_override={"input:0": [1, 224, 224, 3]},
+    )
+    onnx.checker.check_model(model_proto)
+
+    stamp = datetime.datetime.now().strftime("%Y%b").lower()
+    onnx_path = "tensorflow_inception_graph_%s.onnx" % stamp
+    with open(onnx_path, "wb") as f:
+        f.write(model_proto.SerializeToString())
+    print("wrote", onnx_path)
+
+
+if __name__ == "__main__":
+    main()

tensorflow_inception_graph/demo.cpp

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+#include <opencv2/dnn.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/imgcodecs.hpp>
+#include <fstream>
+#include <iostream>
+#include <string>
+#include <vector>
+
+using namespace cv;
+
+static std::string argVal(int argc, char** argv, const std::string& key, const std::string& def)
+{
+    for (int i = 1; i + 1 < argc; ++i)
+        if (key == argv[i]) return argv[i + 1];
+    return def;
+}
+
+int main(int argc, char** argv)
+{
+    std::string model  = argVal(argc, argv, "--model",  "tensorflow_inception_graph_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");
+    std::string labels = argVal(argc, argv, "--labels", "");
+
+    Mat img = imread(image);
+    if (img.empty())
+    {
+        std::cerr << "could not read image: " << image << std::endl;
+        return 1;
+    }
+
+    Mat rgb;
+    cvtColor(img, rgb, COLOR_BGR2RGB);
+    resize(rgb, rgb, Size(224, 224));
+    rgb.convertTo(rgb, CV_32F);
+
+    int dims[] = {1, 224, 224, 3};
+    Mat blob(4, dims, CV_32F, rgb.data);
+
+    dnn::Net net = dnn::readNet(model);
+    net.setInput(blob);
+    Mat scores = net.forward().reshape(1, 1);
+
+    Point classId;
+    double conf;
+    minMaxLoc(scores, 0, &conf, 0, &classId);
+
+    std::string label = std::to_string(classId.x);
+    if (!labels.empty())
+    {
+        std::ifstream f(labels);
+        std::vector<std::string> names;
+        std::string line;
+        while (std::getline(f, line)) names.push_back(line);
+        if (classId.x < (int)names.size()) label = names[classId.x];
+    }
+
+    std::cout << "class " << classId.x << " " << label << " confidence " << conf << std::endl;
+
+    Mat out = img.clone();
+    putText(out, format("%s (%.2f)", label.c_str(), conf), Point(10, 30),
+            FONT_HERSHEY_SIMPLEX, 1.0, Scalar(0, 255, 0), 2);
+    imwrite(output, out);
+    std::cout << "wrote " << output << std::endl;
+    return 0;
+}

tensorflow_inception_graph/demo.py

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+import argparse
+import os
+
+import cv2 as cv
+import numpy as np
+import onnxruntime as ort
+
+here = os.path.dirname(os.path.abspath(__file__))
+
+
+def main():
+    parser = argparse.ArgumentParser(description="TensorFlow Inception (ONNX) image classification demo")
+    parser.add_argument("--model", default=os.path.join(here, "tensorflow_inception_graph_2026jul.onnx"))
+    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"))
+    parser.add_argument("--labels", help="optional ImageNet label file, one class name per line")
+    args = parser.parse_args()
+
+    img = cv.imread(args.image)
+    if img is None:
+        raise SystemExit("could not read image: %s" % args.image)
+
+    rgb = cv.resize(cv.cvtColor(img, cv.COLOR_BGR2RGB), (224, 224)).astype(np.float32)
+
+    sess = ort.InferenceSession(args.model, providers=["CPUExecutionProvider"])
+    scores = sess.run(None, {sess.get_inputs()[0].name: rgb[None]})[0].ravel()
+
+    top = int(np.argmax(scores))
+    conf = float(scores[top])
+    label = str(top)
+    if args.labels:
+        names = open(args.labels).read().splitlines()
+        if top < len(names):
+            label = names[top]
+
+    print("class", top, label, "confidence", round(conf, 4))
+
+    out = img.copy()
+    cv.putText(out, "%s (%.2f)" % (label, conf), (10, 30), cv.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), 2)
+    cv.imwrite(args.output, out)
+    print("wrote", args.output)
+
+
+if __name__ == "__main__":
+    main()

tensorflow_inception_graph/tensorflow_inception_graph_2026jul.onnx

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