efficientnet

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

Google Brain AutoML

NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥

The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

Implementation of EfficientNet model. Keras and TensorFlow Keras.

A PyTorch impl of EfficientDet faithful to the original Google impl w/ ported weights

Pretrained EfficientNet, EfficientNet-Lite, MixNet, MobileNetV3 / V2, MNASNet A1 and B1, FBNet, Single-Path NAS

Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch

Bumble's Private Detector - a pretrained model for detecting lewd images

High level network definitions with pre-trained weights in TensorFlow

MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks. In NeurIPS 2020 workshop.

(Pretrained weights provided) EfficientDet: Scalable and Efficient Object Detection implementation by Signatrix GmbH

A tensorflow2 implementation of some basic CNNs(MobileNetV1/V2/V3, EfficientNet, ResNeXt, InceptionV4, InceptionResNetV1/V2, SENet, SqueezeNet, DenseNet, ShuffleNetV2, ResNet).

A list of popular deep learning models related to classification, segmentation and detection problems

face recognition training project(pytorch)

PyTorch implementation of EfficientNetV2 family

📸 PyTorch implementation of MobileNetV3 for real-time semantic segmentation, with pretrained weights & state-of-the-art performance

🍔🍟🍗 Food analysis baseline with Theseus. Integrate object detection, image classification and multi-class semantic segmentation 🍞🍖🍕

A PyTorch implementation of " EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks."