tensorflow-yolo-v3
tensorflow-lite-YOLOv3
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tensorflow-yolo-v3 | tensorflow-lite-YOLOv3 | |
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3 | 2 | |
894 | 107 | |
- | - | |
0.0 | 0.0 | |
12 months ago | 7 months ago | |
Python | Python | |
Apache License 2.0 | - |
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tensorflow-yolo-v3
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How to use custom model for flutter app?
I used yolov4 and trained model on my classes, then saved weights in file.weights. Now, I want to integrate that model into my flutter app. On firebase, there is an option to use a custom model but that requires uploading .tflite file. My question is how can I convert my trained model weights and upload them as .tflite so could be used in my app. I have tried following this https://github.com/mystic123/tensorflow-yolo-v3 but not success. I would appreciate your help in the conversion of .weights to .tflite or suggest of there is any other way round
- “ValueError: cannot reshape array of size 278540 into shape (256,128,3,3)” Conversion YOLOv3 .weights to .pb
tensorflow-lite-YOLOv3
What are some alternatives?
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
edge-tpu-tiny-yolo - Run Tiny YOLO-v3 on Google's Edge TPU USB Accelerator.
keras-yolo3 - Training and Detecting Objects with YOLO3
faiss-server - faiss serving :)
yolov3-tf2 - YoloV3 Implemented in Tensorflow 2.0
pdf2image - A python module that wraps the pdftoppm utility to convert PDF to PIL Image object
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
YOLOv3 - YOLOv3 Implementation in TensorFlow 1.1X
tflite2tensorflow - Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite, ONNX, OpenVINO, Myriad Inference Engine blob and .pb from .tflite. Support for building environments with Docker. It is possible to directly access the host PC GUI and the camera to verify the operation. NVIDIA GPU (dGPU) support. Intel iHD GPU (iGPU) support. Supports inverse quantization of INT8 quantization model.