ilyas aroui

error: command '/usr/bin/nvcc' failed with exit status 1

3

Hello, When running apex command `python setup.py install --cuda_ext --cpp_ext` I get the following error `error: command '/usr/bin/nvcc' failed with exit status 1` Here's my system details: ``` cudatoolkit: 10.0.130...

Hello, I see that you shared two model weights. There is one trained with Synthext, and another one. My question is, what is the second model trained on ? Thank...

error: command '/usr/bin/nvcc' failed with exit status 1

1

Hello, When running apex command `python setup.py install --cuda_ext --cpp_ext` I get the following error `error: command '/usr/bin/nvcc' failed with exit status 1` I checked some solutions and I removed...

Hello, Why are we using apex ? why are we installing pycocotools ? Thank you

### Search before asking - [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar bug report. ### YOLOv5 Component Export ### Bug Hello, it's compatibility with Android environment...

Hello, Have you tried converting one of EfficientDet Lite family like lite2 found here: [https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1](https://tfhub.dev/tensorflow/efficientdet/lite2/detection/1) using either SavedModel directory or tflite model ? or we we need to build a...

different Normalisation of an input image on mobile (Android) and computer (python)

1

I have an embedder model that takes an RGB image as input and outputs two vectors of shapes `(batch_size,128)` and `(batch_size,512)`. The model was trained with the following preprocessing of...

Implement ELLA for SD1.5

2

This is my draft PR for the bounty **ELLA for SD1.5**. I implemented and tested the architecture. I have few steps left. - [x] Implement ELLA - [x] Test the...

adding NMS to an .mlpackage detection model

6

## ❓Question I have a detection keras model `EfficientDetLite2` That I converted to `mlprogram` as follows: ``` ml_model = ct.convert(keras_model, inputs=[ct.ImageType(name='args_0', shape=(1, 448, 448, 3), scale=1 / 128.0, bias=[-127.0/128.0, -127.0/128.0,...