Comments (2)
Hi,
The model shouldn’t be quantized (i.e. with mlcore-quantization). The script I used to convert the models is
export.py
from mobilepydnet.
I see, my apologies for misunderstanding what you wrote in the paper. If you only used export.py, then how were you able to achieve superior performance (in terms of FPS) compared to FastDepth? Currently, I'm getting ~7 FPS for FastDepth and ~2 FPS for mobilePydnet (192x192 image). After autotuning with TVM and deploying, this jumps to ~4 FPS. Both models tested on one core for a Raspberry Pi 4 overclocked to 2GHz. I see in the paper that you deployed the FastDepth model with the same degree of optimization on the iPhone, yet I would expect slightly better performance for mobilePydnet. Then again, you mention in #1 that your model runs on the GPU; thus, may I assume mobilePydnet is much better fit for inferring on mobile GPUs? I suspect this is the case given that FastDepth is supposed to run on CPUs. I hope to make some scripts and share them with you. Closing for now, feel free to share your insights!
from mobilepydnet.
Related Issues (20)
- what is the range you used for midas output HOT 3
- Dataset release HOT 5
- Do you use BN during training? HOT 1
- How to collect training dataset HOT 2
- About training loss HOT 1
- training loss weight for different scale depth prediction HOT 1
- Can you provide a detailed documentation towards training the model ? HOT 4
- How does mobilePydnet architecture differ from Pydnet architecture HOT 4
- Crash Unexpected failure when preparing tensor allocations - Android 10 (Mi 9T) HOT 8
- Depth image for training HOT 1
- Questions about training pipeline HOT 2
- How to support KPU? HOT 1
- Training Code
- Model Output Issues
- Android Version HOT 3
- Disparity to distance HOT 5
- " Data loss: not an sstable" error when running provided pretrained inference
- About evaluation and loss function HOT 2
- What data augmentation do you use?
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from mobilepydnet.