Pinned Repositories
CasADi_MPC_MHE_Python
This repository is an implementation of the work from Mohamed W. Mehrez. I convert the original code in MATLAB to the Python
mojo
The Mojo Programming Language
nanodet
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
Swin-Transformer
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
tensorrtx
Implementation of popular deep learning networks with TensorRT network definition API
transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
triton
Development repository for the Triton language and compiler
YOLO-Nano
A new version YOLO-Nano
yolov5
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
YOLOv5-Lite
🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 930+kb (int8) and 1.7M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~
Jialeen's Repositories
Jialeen/CasADi_MPC_MHE_Python
This repository is an implementation of the work from Mohamed W. Mehrez. I convert the original code in MATLAB to the Python
Jialeen/mojo
The Mojo Programming Language
Jialeen/nanodet
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
Jialeen/Swin-Transformer
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
Jialeen/tensorrtx
Implementation of popular deep learning networks with TensorRT network definition API
Jialeen/transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Jialeen/triton
Development repository for the Triton language and compiler
Jialeen/YOLO-Nano
A new version YOLO-Nano
Jialeen/yolov5
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Jialeen/YOLOv5-Lite
🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 930+kb (int8) and 1.7M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~