I have 17k labeled images, and i trained them in tensorflow 2 object detection api. and the model was very successful (96% detection rate), and uses 3.5gb ram. I had to convert it trt or onnx in order to run on jetson nano.
But i can not convert this model to onnx or trt in order to run in jetson nano with low ram, high fps.
- How can I manage this conversation and run the model on jetson nano? I’m searching it nearly 3 months and i cannot find any solution. I tried many tutorials and codes. Looked lots of github pages, but I cannot. If you have a good tutorial or knowledge please share with me.
Then, I tried to jetson inference tutorials, and trained ssd-mb1 in pytorch by using train_ssd.py code in jetson inference. I trained the model in my computer nearly 500 epochs
(100 epoch 0.01 lr, then 100 epoch 0.001, then 40 epoch 0.1 lr, then i selected the best in 40 epochs and train it 150 epoch 0.01 lr, finally 100 epoch 0.001lr)
But detection rate of the best models among the 500 .pth file is only 76%.
and it cannot detect any object if object is far.
- How can i improve and make this model good as tensorflow?
sorry to keep busy with simple problems @dusty_nv bu i need
- https://drive.google.com/drive/folders/1StM99xFYTVo4LTXo6-oxdvXz-f_Y5gGq?usp=sharing my TF2 and .pth models