This tutorial provides a comprehensive guide on how to fine-tune a YoloNAS model using a custom dataset. It also demonstrates how to utilize SG's QAT (Quantization-Aware Training) support. Additionally, it offers step-by-step instructions on deploying the model and performing benchmarking. Quanti...
HI, I am trying to load efficientNet. Is there any setting to make model maker only run for tf2. I do not need tf1. Am quite new to custom model training so just following the step. The step dun seem to gave any setting to indicate tf1 or tf2. Is there a better way to do trai...
Additionally, supported convolution neural networks from the PyTorch Image Models (timm) can be specified using timm as a prefix, for example, timm:resnet31 , timm:inception_v4 , timm:efficientnet_b3, and so on. Monitor Metric Specifies the metric that will be monitored while checkpointing and...
Additionally, supported convolution neural networks from the PyTorch Image Models (timm) can be specified using timm as a prefix, for example, timm:resnet31 , timm:inception_v4 , timm:efficientnet_b3, and so on. 1.40625 degrees—This backbone was trained on imagery in which the resolution of...
This tutorial shows you how to train a Pytorch mmdetection object detection model with your custom dataset, and minimal effort on Google Colab Notebook.If you are using my GitHub repo, you probably noticed that mmdetection is included as a submodule, to update that in the future run this ...
Pretrained on more than ImageNetSeveral weights included or references here were pretrained with proprietary datasets that I do not have access to. These include the Facebook WSL, SSL, SWSL ResNe(Xt) and the Google Noisy Student EfficientNet models. The Facebook models have an explicit non-...
<TARGET_MODEL is the model we are trying to attack by training it on the poisoned data. The supported values (corresponding to the paper) are efficientnet (EfficientNetV2), resnext (ResNeXt-101), and vit (FT-ViT). --poison_rate, or π in the paper, is the proportion of the training ...
Additionally, supported convolution neural networks from the PyTorch Image Models (timm) can be specified using timm as a prefix, for example, timm:resnet31 , timm:inception_v4 , timm:efficientnet_b3, and so on. Monitor Metric Specifies the metric that will be monitored while checkpointing and...
_C.MODEL.EFFICIENTNET.NAME = "efficientnet_b0" _C.MODEL.EFFICIENTNET.PRETRAINED = True _C.MODEL.EFFICIENTNET.FEATURE_INDICES = [1, 4, 10, 15] _C.MODEL.EFFICIENTNET.OUT_FEATURES = [ "stride4", "stride8", "stride16", "stride32"] _C.MODEL.EFFICIENTNET.OUT_FEATURES = ["stride4", "st...
We will load the EfficientNet B0 version with the imagenet weights. We will freeze the pre-trained weights of the model so that they do not change while training our model. We will add some custom layers on the top of the model and add our output layer with NUM_CLASSES and activation ...