I want to train YOLOv3 Darknet on a custom dataset using MS COCO pre-trained weights. Specifically, i want to fine-tune the MS COCO pre-trained weights of the YOLOv3 on a custom dataset.
I followed the instructions of data preparation and detector configuration in the AlexeyAB/darknet Github repository, but in pre-trained weights part, only the use of ImageNet pre-trained convolutional weights was explained. I googled about fine-tuning MS COCO pre-trained weights of the YOLOv3 on a custom dataset, but couldn't found any explanation.
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I wanted to know what is a lite model?
I know that a model that is easier to train and has fewer neurons is a lite model but how to say how much are these "fewer neurons"??
If I use a pre-trained model and add two Dense layers to it (where I freeze those pre-trained model layers and train only the Final two layers) can I call these a lite model as it is faster to train and inference results are also fast???
Can someone point me to the Resnet34 pre-trained model on image-net using tensorflow? I am not sure but TF-slim trained model are same or would there be difference?
You can use Keras ResNet(18,34,50,101,152) pre-trained models https://github.com/qubvel/classification_models
I am trying to use the Inception SSD model in tensorflow object detection API. To initialize the weights i want to use pretrained Inception V2 On image net as the feature extractor. I see the model config file lets you use a pretrained model on COCO but if I want to use an Image net model how should I go about it?
To train on Imagenet classification models, do the following:
1) Download a pre-trained model from the "Pre-trained models" section on the Slim page
2) Point the fine_tune_checkpoint at that directory
3) Set from_detection_checkpoint to be false (as you will now be fine-tuning from a classification checkpoint)
Note that training from an Imagenet classification checkpoint will require significantly more time.
I want to implements a faster-rcnn model using distributed tensorflow, But I have difficult to load a pretrained vgg model,How to do it? thanks
The TensorFlow tutorial on retraining inception is a good start to read. Then try to reproduce what it does starting from an already trained vgg model.
Can anyone give an example of how to fine tune a pretrained imagenet network with new data and different classes similar to this:
Fine-tuning a Pretrained Network for Style Recognition
This TensorFlow tutorial describes how to retrain a image classifier for new data and new classes.