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🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Models and examples built with TensorFlow
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
OpenMMLab Detection Toolbox and Benchmark
A book-in-progress about the Linux kernel and its insides.
Best Practices on Recommendation Systems
End-to-End Object Detection with Transformers
A paper list of object detection using deep learning.
Object detection, 3D detection, and pose estimation using center point detection:
Simple Online Realtime Tracking with a Deep Association Metric
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
Keras implementation of RetinaNet object detection.
Deformable DETR: Deformable Transformers for End-to-End Object Detection.
Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
SSD: Single Shot MultiBox Detector | a PyTorch Tutorial to Object Detection
Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning
Simultaneous object detection and tracking using center points.
Pytorch implementation of RetinaNet object detection.
A state-of-the-art semi-supervised method for image recognition
Code & Models for Temporal Segment Networks (TSN) in ECCV 2016
Video classification tools using 3D ResNet
Source code for "On the Relationship between Self-Attention and Convolutional Layers"
Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection, CVPR, Oral, 2020
SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation
A General Toolbox for Identifying Object Detection Errors
A tensorflow implementation of "Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network", a deep learning based Single-Image Super-Resolution (SISR) model.