Flutter On-Device Image Classifier
A Flutter app that captures or picks a photo and classifies it in real time using a bundled TensorFlow Lite model, entirely on-device without network calls. Learners practice camera integration, TFLite model inference, and performance-conscious UI updates.
- Estimate
- ~11h
- Steps
- 6
- Completed by
- 0
- Proposed by
- codeseed.app
Flutter · tflite_flutter · camera
Project roadmap
- 01
Integrate the camera
~1.5hAdd the camera plugin and build a flow for capturing or picking a photo.
- 02
Load the TFLite model
~2hBundle a pretrained image-classification TFLite model into the app assets and load it at startup.
- 03
Preprocess images
~2hResize and normalize captured images into the tensor shape the model expects.
- 04
Run inference
~2hRun the model on the preprocessed image and map output scores to human-readable labels.
- 05
Build the results UI
~1.5hDisplay the top-3 predictions with confidence bars in a clean results screen.
- 06
Optimize performance
~2hMove inference work onto an isolate so the UI thread stays smooth during classification.
Resources
- DocsDart Guides
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Tech stack