TFLiteClassification
mmpretrain
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TFLiteClassification | mmpretrain | |
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1 | 2 | |
1 | 3,156 | |
- | 4.1% | |
0.0 | 7.8 | |
about 3 years ago | 9 days ago | |
Jupyter Notebook | Python | |
- | Apache License 2.0 |
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TFLiteClassification
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TensorFlow Lite Python Interpreter Implementation!
TFLiteClassification
mmpretrain
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMClassification: OpenMMLab image classification toolbox and benchmark.
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how to recognize digits from this pics(i have many of them) so kindly suggest generic that can work for other similar images. I have searched alot for the source code on github but not found the correct solution. most of these solutions were incorrect while other were incomplete. Kindly help me :(
MMClassification or TIMM would be good starting points for training a classification model.
What are some alternatives?
poolformer - PoolFormer: MetaFormer Is Actually What You Need for Vision (CVPR 2022 Oral)
efficientnet - Implementation of EfficientNet model. Keras and TensorFlow Keras.
rpi-urban-mobility-tracker - The easiest way to count pedestrians, cyclists, and vehicles on edge computing devices or live video feeds.
ModelZoo.pytorch - Hands on Imagenet training. Unofficial ModelZoo project on Pytorch. MobileNetV3 Top1 75.64🌟 GhostNet1.3x 75.78🌟
automl - Google Brain AutoML
senet.pytorch - PyTorch implementation of SENet
PAWS-TF - Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.
AdelaiDet - AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
BERT-for-Mobile - Compares the DistilBERT and MobileBERT architectures for mobile deployments.
pytorch-image-models - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
node-efficientnet - tensorflowJS implementation of EfficientNet 🚀
biprop - Identify a binary weight or binary weight and activation subnetwork within a randomly initialized network by only pruning and binarizing the network.