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A flutter plugin for run object detection and image classifications from pytorch models like yolov5.

flutter_pytorch #

  • flutter plugin to help run pytorch lite models classification for example yolov5 doesn't give support for yolov7
  • ios support (can be added following this https://github.com/pytorch/ios-demo-app) PR will be appreciated

Getting Started #

Usage #

preparing the model #

  • classification
import torch
from torch.utils.mobile_optimizer import optimize_for_mobile


model = torch.load('model_scripted.pt',map_location="cpu")
model.eval()
example = torch.rand(1, 3, 224, 224)
traced_script_module = torch.jit.trace(model, example)
optimized_traced_model = optimize_for_mobile(traced_script_module)
optimized_traced_model._save_for_lite_interpreter("model.pt")
  • object detection (yolov5)
!python export.py --weights "the weights of your model" --include torchscript --img 640 --optimize

example

!python export.py --weights yolov5s.pt --include torchscript --img 640 --optimize

Installation #

To use this plugin, add pytorch_lite as a dependency in your pubspec.yaml file.

Create a assets folder with your pytorch model and labels if needed. Modify pubspec.yaml accordingly.

assets:
 - assets/models/model_classification.pt
 - assets/labels_classification.txt
 - assets/models/model_objectDetection.torchscript
 - assets/labels_objectDetection.txt

Run flutter pub get

For release

  • Go to android/app/build.gradle
  • Add those next lines in the release config
shrinkResources false
minifyEnabled false

example

    buildTypes {
        release {
            shrinkResources false
            minifyEnabled false
            // TODO: Add your own signing config for the release build.
            // Signing with the debug keys for now, so `flutter run --release` works.
            signingConfig signingConfigs.debug
        }
    }

Import the library #

import 'package:flutter_pytorch/flutter_pytorch.dart';

Load model #

Either classification model:

ClassificationModel classificationModel= await FlutterPytorch.loadClassificationModel(
          "assets/models/model_classification.pt", 224, 224,
          labelPath: "assets/labels/label_classification_imageNet.txt");

Or objectDetection model:

ModelObjectDetection objectModel = await FlutterPytorch.loadObjectDetectionModel(
          "assets/models/yolov5s.torchscript", 80, 640, 640,
          labelPath: "assets/labels/labels_objectDetection_Coco.txt");

Get classification prediction as label #

String imagePrediction = await classificationModel.getImagePrediction(await File(image.path).readAsBytes());

Get classification prediction as raw output layer #

List<double?>? predictionList = await _imageModel!.getImagePredictionList(
      await File(image.path).readAsBytes(),
    );

Get classification prediction as Probabilities (incase model is not using softmax) #

List<double?>? predictionListProbabilites = await _imageModel!.getImagePredictionListProbabilities(
      await File(image.path).readAsBytes(),
    );

Get object detection prediction for an image #

 List<ResultObjectDetection?> objDetect = await _objectModel.getImagePrediction(await File(image.path).readAsBytes(),
        minimumScore: 0.1, IOUThershold: 0.3);

Get render boxes with image #

objectModel.renderBoxesOnImage(_image!, objDetect)

Image prediction for an image with custom mean and std #

final mean = [0.5, 0.5, 0.5];
final std = [0.5, 0.5, 0.5];
String prediction = await classificationModel
        .getImagePrediction(image, mean: mean, std: std);

#References

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A flutter plugin for run object detection and image classifications from pytorch models like yolov5.

Repository (GitHub)
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Documentation

API reference

License

GPL-3.0 (LICENSE)

Dependencies

flutter, path, path_provider, pigeon, plugin_platform_interface

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