ACSL This repo is the official implementation for CVPR 2021 paper: Adaptive Class Suppression Loss for Long-Tail Object Detection. Requirements 1. Environment: The requirements are exactly the same as BalancedGroupSoftmax. We tested on the following settings: python 3.7 cuda 10.0 pytorch 1.2.0 torchvision 0.4.0 mmcv 0.2.14 conda create -n mmdet python=3.7 -y conda activate mmdet pip install cython pip install numpy pip install torch pip install torchvision pip install pycocotools pip install matplotlib pip install terminaltables # download the source […]

carefree-learn carefree-learn is a minimal Automatic Machine Learning (AutoML) solution for tabular datasets based on PyTorch. Carefree? carefree-learn aims to provide CAREFREE usages for both users and developers. User Side import cflearn import numpy as np x = np.random.random([1000, 10]) y = np.random.random([1000, 1]) m = cflearn.make().fit(x, y) Developer Side import cflearn import numpy as np cflearn.register_model(“wnd_full”, pipes=[cflearn.PipeInfo(“fcnn”), cflearn.PipeInfo(“linear”)]) x = np.random.random([1000, 10]) y = np.random.random([1000, 1]) m = cflearn.make(“wnd_full”).fit(x, y) Please refer to Quick Start and Build Your Own […]

swin-transformer-pytorch Implementation of the Swin Transformer architecture. This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with shifted […]

STARK The official implementation of the paper Learning Spatio-Temporal Transformer for Visual Tracking Hiring research interns for visual transformer projects: [email protected] Highlights End-to-End, Post-processing Free STARK is an end-to-end tracking approach, which directly predicts one accurate bounding box as the tracking result.Besides, STARK does not use any hyperparameters-sensitive post-processing, leading to stable performances. Real-Time Speed STARK-ST50 and STARK-ST101 run at 40FPS and 30FPS respectively on a Tesla V100 GPU. Strong performance Tracker LaSOT (AUC) GOT-10K (AO) TrackingNet (AUC) STARK 67.1 […]