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Yolo-v7

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metadata
library_name: pytorch
license: other
tags:
  - real_time
  - android
pipeline_tag: object-detection

Yolo-v7: Optimized for Mobile Deployment

Real-time object detection optimized for mobile and edge

YoloV7 is a machine learning model that predicts bounding boxes and classes of objects in an image.

This model is an implementation of Yolo-v7 found here.

This repository provides scripts to run Yolo-v7 on Qualcomm® devices. More details on model performance across various devices, can be found here.

WARNING: The model assets are not readily available for download due to licensing restrictions.

Model Details

  • Model Type: Model_use_case.object_detection
  • Model Stats:
    • Model checkpoint: YoloV7 Tiny
    • Input resolution: 640x640
    • Number of parameters: 6.24M
    • Model size (float): 23.8 MB
    • Model size (w8a8): 6.23 MB
    • Model size (w8a16): 6.66 MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
Yolo-v7 float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 24.69 ms 1 - 118 MB NPU --
Yolo-v7 float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 23.017 ms 5 - 136 MB NPU --
Yolo-v7 float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 12.899 ms 1 - 48 MB NPU --
Yolo-v7 float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 13.272 ms 5 - 43 MB NPU --
Yolo-v7 float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 9.495 ms 0 - 100 MB NPU --
Yolo-v7 float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 10.461 ms 3 - 32 MB NPU --
Yolo-v7 float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) ONNX 10.52 ms 0 - 48 MB NPU --
Yolo-v7 float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 11.354 ms 1 - 117 MB NPU --
Yolo-v7 float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 10.502 ms 2 - 130 MB NPU --
Yolo-v7 float SA7255P ADP Qualcomm® SA7255P TFLITE 24.69 ms 1 - 118 MB NPU --
Yolo-v7 float SA7255P ADP Qualcomm® SA7255P QNN_DLC 23.017 ms 5 - 136 MB NPU --
Yolo-v7 float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 9.471 ms 0 - 101 MB NPU --
Yolo-v7 float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 10.432 ms 5 - 21 MB NPU --
Yolo-v7 float SA8295P ADP Qualcomm® SA8295P TFLITE 14.446 ms 1 - 40 MB NPU --
Yolo-v7 float SA8295P ADP Qualcomm® SA8295P QNN_DLC 11.804 ms 2 - 42 MB NPU --
Yolo-v7 float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 9.481 ms 0 - 102 MB NPU --
Yolo-v7 float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 10.461 ms 5 - 21 MB NPU --
Yolo-v7 float SA8775P ADP Qualcomm® SA8775P TFLITE 11.354 ms 1 - 117 MB NPU --
Yolo-v7 float SA8775P ADP Qualcomm® SA8775P QNN_DLC 10.502 ms 2 - 130 MB NPU --
Yolo-v7 float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 9.408 ms 0 - 103 MB NPU --
Yolo-v7 float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 10.539 ms 5 - 21 MB NPU --
Yolo-v7 float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 10.612 ms 0 - 40 MB NPU --
Yolo-v7 float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 6.7 ms 8 - 221 MB NPU --
Yolo-v7 float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 6.008 ms 5 - 313 MB NPU --
Yolo-v7 float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 7.683 ms 5 - 227 MB NPU --
Yolo-v7 float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 7.059 ms 1 - 116 MB NPU --
Yolo-v7 float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 5.878 ms 5 - 128 MB NPU --
Yolo-v7 float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 6.636 ms 5 - 136 MB NPU --
Yolo-v7 float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 11.142 ms 210 - 210 MB NPU --
Yolo-v7 float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 12.352 ms 8 - 8 MB NPU --
Yolo-v7 w8a16 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 18.123 ms 2 - 37 MB NPU --
Yolo-v7 w8a16 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 12.229 ms 2 - 54 MB NPU --
Yolo-v7 w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 9.433 ms 2 - 16 MB NPU --
Yolo-v7 w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) ONNX 120.182 ms 17 - 382 MB NPU --
Yolo-v7 w8a16 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 10.283 ms 2 - 39 MB NPU --
Yolo-v7 w8a16 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN_DLC 23.85 ms 2 - 43 MB NPU --
Yolo-v7 w8a16 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) ONNX 210.86 ms 85 - 100 MB CPU --
Yolo-v7 w8a16 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) ONNX 184.504 ms 77 - 88 MB CPU --
Yolo-v7 w8a16 SA7255P ADP Qualcomm® SA7255P QNN_DLC 18.123 ms 2 - 37 MB NPU --
Yolo-v7 w8a16 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 9.405 ms 2 - 15 MB NPU --
Yolo-v7 w8a16 SA8295P ADP Qualcomm® SA8295P QNN_DLC 11.982 ms 2 - 50 MB NPU --
Yolo-v7 w8a16 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 9.433 ms 2 - 14 MB NPU --
Yolo-v7 w8a16 SA8775P ADP Qualcomm® SA8775P QNN_DLC 10.283 ms 2 - 39 MB NPU --
Yolo-v7 w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 9.443 ms 2 - 18 MB NPU --
Yolo-v7 w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 122.948 ms 43 - 373 MB NPU --
Yolo-v7 w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 5.124 ms 2 - 53 MB NPU --
Yolo-v7 w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 92.731 ms 37 - 1273 MB NPU --
Yolo-v7 w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 4.979 ms 2 - 46 MB NPU --
Yolo-v7 w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 91.176 ms 54 - 1814 MB NPU --
Yolo-v7 w8a16 Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 7.915 ms 12 - 12 MB NPU --
Yolo-v7 w8a16 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 116.555 ms 58 - 58 MB NPU --
Yolo-v7 w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 5.567 ms 0 - 27 MB NPU --
Yolo-v7 w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 4.303 ms 1 - 30 MB NPU --
Yolo-v7 w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 3.315 ms 0 - 49 MB NPU --
Yolo-v7 w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 2.742 ms 1 - 47 MB NPU --
Yolo-v7 w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 2.617 ms 0 - 32 MB NPU --
Yolo-v7 w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 1.914 ms 1 - 11 MB NPU --
Yolo-v7 w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) ONNX 3.839 ms 0 - 93 MB NPU --
Yolo-v7 w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 3.053 ms 0 - 27 MB NPU --
Yolo-v7 w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 2.361 ms 1 - 31 MB NPU --
Yolo-v7 w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) TFLITE 19.88 ms 8 - 56 MB NPU --
Yolo-v7 w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN_DLC 8.683 ms 1 - 38 MB NPU --
Yolo-v7 w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) ONNX 57.963 ms 38 - 54 MB CPU --
Yolo-v7 w8a8 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) TFLITE 114.496 ms 18 - 48 MB GPU --
Yolo-v7 w8a8 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) ONNX 50.506 ms 35 - 46 MB CPU --
Yolo-v7 w8a8 SA7255P ADP Qualcomm® SA7255P TFLITE 5.567 ms 0 - 27 MB NPU --
Yolo-v7 w8a8 SA7255P ADP Qualcomm® SA7255P QNN_DLC 4.303 ms 1 - 30 MB NPU --
Yolo-v7 w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 2.621 ms 0 - 8 MB NPU --
Yolo-v7 w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 1.918 ms 1 - 14 MB NPU --
Yolo-v7 w8a8 SA8295P ADP Qualcomm® SA8295P TFLITE 4.344 ms 0 - 35 MB NPU --
Yolo-v7 w8a8 SA8295P ADP Qualcomm® SA8295P QNN_DLC 3.251 ms 1 - 37 MB NPU --
Yolo-v7 w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 2.626 ms 0 - 33 MB NPU --
Yolo-v7 w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 1.912 ms 0 - 9 MB NPU --
Yolo-v7 w8a8 SA8775P ADP Qualcomm® SA8775P TFLITE 3.053 ms 0 - 27 MB NPU --
Yolo-v7 w8a8 SA8775P ADP Qualcomm® SA8775P QNN_DLC 2.361 ms 1 - 31 MB NPU --
Yolo-v7 w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 2.668 ms 0 - 22 MB NPU --
Yolo-v7 w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN_DLC 1.915 ms 1 - 13 MB NPU --
Yolo-v7 w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 3.864 ms 0 - 103 MB NPU --
Yolo-v7 w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 1.752 ms 0 - 40 MB NPU --
Yolo-v7 w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 1.287 ms 1 - 47 MB NPU --
Yolo-v7 w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 2.718 ms 0 - 348 MB NPU --
Yolo-v7 w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 1.608 ms 0 - 29 MB NPU --
Yolo-v7 w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN_DLC 1.175 ms 1 - 37 MB NPU --
Yolo-v7 w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 2.567 ms 1 - 131 MB NPU --
Yolo-v7 w8a8 Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 2.188 ms 22 - 22 MB NPU --
Yolo-v7 w8a8 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 3.848 ms 6 - 6 MB NPU --

Installation

Install the package via pip:

pip install "qai-hub-models[yolov7]"

Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub with your Qualcomm® ID. Once signed in navigate to Account -> Settings -> API Token.

With this API token, you can configure your client to run models on the cloud hosted devices.

qai-hub configure --api_token API_TOKEN

Navigate to docs for more information.

Demo off target

The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input.

python -m qai_hub_models.models.yolov7.demo

The above demo runs a reference implementation of pre-processing, model inference, and post processing.

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.yolov7.demo

Run model on a cloud-hosted device

In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following:

  • Performance check on-device on a cloud-hosted device
  • Downloads compiled assets that can be deployed on-device for Android.
  • Accuracy check between PyTorch and on-device outputs.
python -m qai_hub_models.models.yolov7.export

How does this work?

This export script leverages Qualcomm® AI Hub to optimize, validate, and deploy this model on-device. Lets go through each step below in detail:

Step 1: Compile model for on-device deployment

To compile a PyTorch model for on-device deployment, we first trace the model in memory using the jit.trace and then call the submit_compile_job API.

import torch

import qai_hub as hub
from qai_hub_models.models.yolov7 import Model

# Load the model
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S24")

# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()

pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])

# Compile model on a specific device
compile_job = hub.submit_compile_job(
    model=pt_model,
    device=device,
    input_specs=torch_model.get_input_spec(),
)

# Get target model to run on-device
target_model = compile_job.get_target_model()

Step 2: Performance profiling on cloud-hosted device

After compiling models from step 1. Models can be profiled model on-device using the target_model. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics.

profile_job = hub.submit_profile_job(
    model=target_model,
    device=device,
)

Step 3: Verify on-device accuracy

To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device.

input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
    model=target_model,
    device=device,
    inputs=input_data,
)
    on_device_output = inference_job.download_output_data()

With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output.

Note: This on-device profiling and inference requires access to Qualcomm® AI Hub. Sign up for access.

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.yolov7.demo --eval-mode on-device

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.yolov7.demo -- --eval-mode on-device

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

  • TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

  • QNN (.so export ): This sample app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on Yolo-v7's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of Yolo-v7 can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

Community