Projects sharing features with HRNet Semantic Segmentation

The table prvoides the models and results of various models on CIFAR100. Learning rate =0.1 and will be divided by 10 every 70 epochs. Total 300 epochs. Using SGD optimizer, momentum=0.9, weight_decay=5e-4. Loss is CrossEntropyLoss. Batch-size=512.

Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying neural networks across distributed systems, mobile devices, and hardware accelerators. It functions as a cross-platform runtime and a dynamic dataflow scheduler that optimizes neural network execution. The framework provides a multi-language API, enabling the development of machine learning models using Python, R, Julia, Scala, Go, and JavaScript. It supports high-performance model training and the scaling of workloads across multiple GPUs and machines. The system covers cap

A repository for the code used to create and train the model defined in 'Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification' from AAAI 2021 (See the Paper section).

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

Code for the paper MSAF: Multimodal Split Attention Fusion. This is our implementation of the MSAF module and the three MSAF-powered multimodal networks.

Keras implementation of Deeplab v3+ with pretrained weights

PyTorch implementation of the paper "FcaNet: Frequency Channel Attention Networks".

MobileSAM is a lightweight image segmenter and promptable vision model designed for fast object isolation on resource-constrained hardware. It functions as an automatic image masking tool capable of detecting and isolating distinct objects across an entire image without manual input. The system enables prompt-based object masking using coordinate points or bounding boxes to generate precise masks. It also supports all-object image segmentation through object-aware prompt sampling to identify every distinct object in a scene. To facilitate mobile and edge deployment, the model is compatible w

Mask RCNN in TensorFlow

The official PyTorch code implementation for TIP20' Submission: "Person Re-identification via Attention Pyramid"

This is a PyTorch implementation of CoAtNet specified in "CoAtNet: Marrying Convolution and Attention for All Data Sizes", arXiv 2021.

The PyTorch implementation of Att-DARTS: Differentiable Neural Architecture Search for Attention.

Official PyTorch implementation of "EdgeSAM: Prompt-In-the-Loop Distillation for On-Device Deployment of SAM"

An implementation of the efficient attention module.

BiSeNetV2 is faster and requires less memory, you can try BiSeNetV2 on cityscapes like this: ` $ export CUDAVISIBLEDEVICES=0,1 $ python -m torch.distributed.launch --nprocpernode=2 bisenetv2/train.py --fp16 ` This would train the model and then compute the mIOU on eval set.

This repository is a PyTorch implementation of our coordinate attention (will appear in CVPR2021).