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Requirements
Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # installEnvironments
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glenn-jocher
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YOLOv5 Component
Export
Bug
Hi, I am trying to export the yolov5s model to Keras.
I have followed the guide on export. First I tried copying the command as it is suggested (python export.py --weights yolov5s.pt --include saved_model), but when trying to use it, it says the model is a '_UserObject' and doesn't have the attributes I expect from a keras model.
I found this issue that talks about it, and the solution was to add the --keras flag to the export command. Now the model, which was loaded with type <class 'keras.engine.functional.Functional'>, raises a "NotImplementedError" when trying to do model.to_json or saving the model.
I don't understand what is wrong with the model.
The model summary looks like this: Model: "model"
Layer (type) Output Shape Param # Connected to
input_1 (InputLayer) [(1, 640, 640, 3)] 0 []
tf_conv (TFConv) (1, 320, 320, 32) 3488 ['input_1[0][0]']
tf_conv_1 (TFConv) (1, 160, 160, 64) 18496 ['tf_conv[0][0]']
tfc3 (TFC3) (1, 160, 160, 64) 18624 ['tf_conv_1[0][0]']
tf_conv_7 (TFConv) (1, 80, 80, 128) 73856 ['tfc3[0][0]']
tfc3_1 (TFC3) (1, 80, 80, 128) 115200 ['tf_conv_7[0][0]']
tf_conv_15 (TFConv) (1, 40, 40, 256) 295168 ['tfc3_1[0][0]']
tfc3_2 (TFC3) (1, 40, 40, 256) 623872 ['tf_conv_15[0][0]']
tf_conv_25 (TFConv) (1, 20, 20, 512) 1180160 ['tfc3_2[0][0]']
tfc3_3 (TFC3) (1, 20, 20, 512) 1181184 ['tf_conv_25[0][0]']
tfsppf (TFSPPF) (1, 20, 20, 512) 656128 ['tfc3_3[0][0]']
tf_conv_33 (TFConv) (1, 20, 20, 256) 131328 ['tfsppf[0][0]']
tf_upsample (TFUpsample) (1, 40, 40, 256) 0 ['tf_conv_33[0][0]']
tf_concat (TFConcat) (1, 40, 40, 512) 0 ['tf_upsample[0][0]',
'tfc3_2[0][0]']
tfc3_4 (TFC3) (1, 40, 40, 256) 361216 ['tf_concat[0][0]']
tf_conv_39 (TFConv) (1, 40, 40, 128) 32896 ['tfc3_4[0][0]']
tf_upsample_1 (TFUpsample) (1, 80, 80, 128) 0 ['tf_conv_39[0][0]']
tf_concat_1 (TFConcat) (1, 80, 80, 256) 0 ['tf_upsample_1[0][0]',
'tfc3_1[0][0]']
tfc3_5 (TFC3) (1, 80, 80, 128) 90496 ['tf_concat_1[0][0]']
tf_conv_45 (TFConv) (1, 40, 40, 128) 147584 ['tfc3_5[0][0]']
tf_concat_2 (TFConcat) (1, 40, 40, 256) 0 ['tf_conv_45[0][0]',
'tf_conv_39[0][0]']
tfc3_6 (TFC3) (1, 40, 40, 256) 295680 ['tf_concat_2[0][0]']
tf_conv_51 (TFConv) (1, 20, 20, 256) 590080 ['tfc3_6[0][0]']
tf_concat_3 (TFConcat) (1, 20, 20, 512) 0 ['tf_conv_51[0][0]',
'tf_conv_33[0][0]']
tfc3_7 (TFC3) (1, 20, 20, 512) 1181184 ['tf_concat_3[0][0]']
tf_detect (TFDetect) ((1, 25200, 85), 229245 ['tfc3_5[0][0]',
) 'tfc3_6[0][0]',
'tfc3_7[0][0]']
================================================================================================== Total params: 7,225,885 Trainable params: 0 Non-trainable params: 7,225,885
Environment
OS - Ubuntu 18.04.6 LTS Yolov -
Conda environment: Python - 3.7.15 Tensorflow - 2.11.0
Minimal Reproducible Example
python export.py --weights yolov5s.pt --include saved_model --keras
import tensorflow as tf k_model = tf.keras.models.load_model("./yolov5s_saved_model") print(k_model.summary())
tf.keras.models.save_model(k_model, "my_h5_model.h5")
Additional
No response
Are you willing to submit a PR?