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.
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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.
Is it possible to use CenterNet with MobilenetV2 backbone (TF Lite compatible) to perform keypoints detection on a custom dataset? Is there a tutorial somewhere?
I have something for training keypoint detection model for custom dataset on Centernet model with hourglass backbone.
This github repo Custom Keypoint Detection for dataset preparation, model training and inference on Centernet-hourglass104 keypoint detection model based on Tensorflow Object detection API with examples.
This could help you in training your keypoint detection model on custom dataset.
Any issues related to the project can be raised in the github itself and doubts can be cleared here.
I need to Load a pre-trained model into Keras without the Weight and bias. I
also just want to use the Architecture of the model alone for my training.
Example:
I want to load coco_mobilenet model pre-trained without Weights and bias.
Any suggestions would be appreciated.
net=keras.applications.MobileNet(weight=None)
net.summary()
view keras mobilenet api for detail
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.