Please provide the official input format specifications and constraints

NeuroPilot NeuroPilot - Analytical AI
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1

During the model conversion pipeline .pt → .tflite → .dla, the input specification requirements may change at each conversion stage. Therefore, the input format used for the original .pt model cannot be directly applied to the .dla model, and a dedicated description of the input format specification for the .dla model is required.

The conversion from .tflite to .dla is performed using the SDK tool:

/neuropilot-sdk-premium-8.0.7-build20250122/neuron_sdk/host/bin/ncc-tflite

Please provide the official input format specifications and constraints defined by this SDK.

3

Hi Wr.Zeng,

Thanks for reaching out!

For the PyTorch side, the accepted input model format is TorchScript (i.e., a ScriptModule object or file). For more details about the supported model input and output formats, please refer to the Supported Model Input and Output Formats section in the NeuroPilot documentation.

Best,
Jun

5

The conversion from .pt to .tflite works well and can be verified by our validation script.But when the .tflite model is converted to DLA, the inference output is inaccurate. so we need a dedicated description of the input format specification for the .dla model is require

6

The conversion from .pt to .tflite works well and can be verified by our validation script.But when the .tflite model is converted to DLA, the inference output is inaccurate. so we need a dedicated description of the input format specification for the .dla model is require

7

Hi Wr.Zeng,

To help us pinpoint the issue, could you share a bit more about your setup? Specifically:

  • Model data type : Is your TFLite model in FP32, FP16, INT16, or INT8?
  • ncc-tflite compile command : Which options did you use? (e.g., --arch, --relax-fp32, --suppress-input/output)
  • How the inaccuracy is observed:
    • What’s your reference for comparison: the TFLite interpreter output, or the original PyTorch output?
    • Is it a complete mismatch, or just a small numerical deviation?
  • Input data flow — How is the input fed into the DLA? Did you apply the same pre-processing (and quantization scale / zero_point if applicable) as the TFLite model?

Quick note: a quant-8 TFLite → quant-8 DLA conversion via ncc-tflite normally shouldn’t drop much accuracy, so once we know more about your setup, it’ll be easier to figure out where things went off.

Best,
Jun

8

Thank you for answer. I’m currently using the INT8 quantized model downloaded from here: YOLOv8s Models — IoT AI Hub documentation . Could you let me know what configurations were used for the ncc-tflite compile command? For reference, my current command is: ./neuropilot-sdk-premium-8.0.7-build20250122/neuron_sdk/host/bin/ncc-tflite --arch=mdla5.3,edma3.6 yolov8s_quant.tflite."

9

Hi Wr.Zeng,

Here is the compile command for generating the DLA from that URL:

./ncc-tflite --arch=mdla5.3,edma3.6 {model.tflite} -o {model.dla}

The NeuroPilot SDK version used is basic-8.0.10-build20251029.

Best,
Jun

10

Please help me confirm if the following is correct

yolo export model=yolov8s.pt format=torchscript
convert to tflite :

import mtk_converter
import numpy as np

def data_gen():
for i in range(100):
yield [np.random.randn(1, 3, 640, 640).astype(np.float32)]

converter = mtk_converter.PyTorchConverter.from_script_module_file(
‘yolov8s.torchscript’, [[1, 3, 640, 640]],
)
converter.quantize = True
converter.input_value_ranges = [(-1.0, 1.0)]
converter.calibration_data_gen = data_gen
_ = converter.convert_to_tflite(output_file=‘yolov8s_quant.tflite’,tflite_op_export_spec=‘builtin_ignore_version’)

11

Hi Wr_Zeng,

Your code for converting to TFLite is the same as the AI Hub reference.
You can visit Quant8 Conversion Process for more details.

Best,
Jun

12

我按那上面来。执行这一步python3 export.py --weights yolov8s.pt --img-size 640 640 --include torchscript最是报错。问AI说yolo export model=yolov8s.pt format=torchscript

13

I folow Quant8 Conversion Process .but exec the following step:python3 export.py --weights yolov8s.pt --img-size 640 640 --include torchscrip it is alway error. so I ask AI. AI tell me:yolo export model=yolov8s.pt format=torchscript

14

I user neuropilot-sdk-premium-8.0.7-build20250122 ,is it also OK ?

15

Hi Wr_Zeng,

Before confirming whether yolo export model=yolov8s.pt format=torchscript is a viable path, could you help clarify a couple of things?

  1. Precondition steps — did you follow them exactly?

    • Cloned the YOLOv5 repo and ran git reset --hard 485da42?
    • Using torch==1.9.0 and torchvision==0.10.0?
    • What’s your Python version and OS?

  2. SDK version — the AI Hub reference uses neuropilot-sdk-premium-8.0.10-build20251029. Your 8.0.7-build20250122 might work, but to rule out version-related accuracy differences in the .tflite → .dla step, I’d recommend aligning with the reference version.

Quick note — yolo export ... (Ultralytics CLI) and python3 export.py ... (YOLOv5 repo at commit 485da42) may produce TorchScript files with different internal structures. Even if yolo export succeeds, the downstream tflite conversion or DLA accuracy could behave differently from the AI Hub reference flow.

Best,
Jun

16

Ai hub cann’t found neuropilot-sdk-premium-8.0.10-build20251029.only found neuropilot-sdk-basic-8.0.11-build20260211.tar.gz .can you provide download link to me ?

Thanks