Update README.md

README.md

@@ -1,3 +1,47 @@
 ---
 license: apache-2.0
----
+---
+
+## I made the dynamic the model:
+face_detection_yunet_2023mar.onnx
+https://github.com/opencv/opencv_zoo/blob/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx
+
+
+## The way y Made it dynamic:
+
+```
+# Initialization
+!pip install onnx==1.16 | tail -n 1
+!pip install onnxruntime==1.16.3 | tail -n 1
+
+import onnx
+import onnxruntime as ort
+from onnx.tools import update_model_dims
+
+# We read the model
+onnx_model_path = "face_detection_yunet_2023mar.onnx"
+model = onnx.load(onnx_model_path)
+## Vemos los dims
+input_dims = {i.name:  [d.dim_value for d in i.type.tensor_type.shape.dim] for i in model.graph.input}
+output_dims = {o.name:  [d.dim_value for d in o.type.tensor_type.shape.dim] for o in model.graph.output}
+print(input_dims)
+print(output_dims)
+
+# Debug: we create a session (not needed)
+ort.set_default_logger_severity(3)
+ort_session = ort.InferenceSession(onnx_model_path, providers=['CPUExecutionProvider'])
+print("Original Input Shapes:")
+for i in ort_session.get_inputs():
+    print(f"Input Name: {i.name}, Shape: {i.shape}")
+
+# Here we made the model dynamic. The character "N" means the input is dynamic
+input_dims['input'] = [1, 3, "N", "N"]
+updated_model = update_model_dims.update_inputs_outputs_dims(model, input_dims, output_dims)
+
+# Finally we save the dynamic model
+import os
+os.mkdir("./save_dir")
+dynamic_model_path = "./save_dir/face_detection_yunet_2023mar_dynamic.onnx"
+onnx.save(updated_model, dynamic_model_path)
+
+```