tensorrtx
tensorflow-yolov4-tflite
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| tensorrtx | tensorflow-yolov4-tflite | |
|---|---|---|
| 3 | 4 | |
| 6,584 | 2,223 | |
| - | - | |
| 8.4 | 0.0 | |
| 2 days ago | 11 months ago | |
| C++ | Python | |
| MIT License | MIT License |
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tensorrtx
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A Three-pronged Approach to Bringing ML Models Into Production
In terms of the latter, this is quite common when employing non-standard SOTA models. You may discover a variety of TensorRT implementations on the web if you want to use popular models—for example, in the project where we needed to train an object-detection algorithm on Rutorch and deploy it on Triton, we used many cases of PyTorch -> TensorRT -> Triton. The implementation of the model on TensoRT was taken from here. You may also be interested in this repository, as it contains many current implementations supported by developers.
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Dall-E 2
I'll try them out. I have an RTX 2070, which apparently supports fp16. But it only has 8GB RAM.
I used the instructions here to check: https://github.com/wang-xinyu/tensorrtx/blob/master/tutorial...
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Increasing usb cam FPS with Yolov5 on a Jetson Xavier NX?
Optimize your model using TensorRT. There is a good implementation here: https://github.com/wang-xinyu/tensorrtx/tree/master/yolov5
tensorflow-yolov4-tflite
- Object tracking on Android
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Tensorflow Yolo Implementation
Hello all, I've been desperate for help in the tensorflow yolo objection detection framework. Basically, in https://github.com/hunglc007/tensorflow-yolov4-tflite 's implementation, there is problem whereby with GPU, it only detects the first frame in the whole video while CPU works fine but absolutely slow. There are issues opened at https://github.com/hunglc007/tensorflow-yolov4-tflite/issues/282 but no proper solution is found. I've also opened a question stackoverflow: https://stackoverflow.com/questions/68333281/tensorflow-yolov4-detect-video
- “ValueError: cannot reshape array of size 278540 into shape (256,128,3,3)” Conversion YOLOv3 .weights to .pb
What are some alternatives?
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
v-diffusion-pytorch - v objective diffusion inference code for PyTorch.
YOLOX - YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
dalle-mini - DALL·E Mini - Generate images from a text prompt
edge-tpu-tiny-yolo - Run Tiny YOLO-v3 on Google's Edge TPU USB Accelerator.
dalle-2-preview
tensorflow-lite-YOLOv3 - YOLOv3: convert .weights to .tflite format for tensorflow lite. Convert .weights to .pb format for tensorflow serving
SegmentationCpp - A c++ trainable semantic segmentation library based on libtorch (pytorch c++). Backbone: VGG, ResNet, ResNext. Architecture: FPN, U-Net, PAN, LinkNet, PSPNet, DeepLab-V3, DeepLab-V3+ by now.
Swin-Transformer-Tensorflow - Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)
gpt-2 - Code for the paper "Language Models are Unsupervised Multitask Learners"
yolo-tensorrt - TensorRT8.Support Yolov5n,s,m,l,x .darknet -> tensorrt. Yolov4 Yolov3 use raw darknet *.weights and *.cfg fils. If the wrapper is useful to you,please Star it.