I have an implementation of YOLOv3 using mobilenet working (based on https://github.com/Adamdad/keras-YOLOv3-mobilenet). However, I already use mobilenet for feature extraction on other functionalities. I would like to reuse these features extracted from the images to the other models. How can I get them from YOLOv3 model already trained? Is it possible to get the vectors from an intermediate layer of the model?
I've tried this with no success: https://keras.io/getting_started/faq/#how-can-i-obtain-the-output-of-an-intermediate-layer-feature-extraction
Thanks
Related
I am trying to find a pre-trained model that will classify images based on if there is a human present in the photo or not.
You can use the models trained on the COCO dataset for this.
For example, for Pytorch you can have a look at the official documentation concerning the provided models here.
There are more variety of models if you give it a simple search both for Pytorch and other frameworks.
You can check out the COCO homepage if you need more information concerning the dataset and the tasks it supports.
You may also find These useful:
Detecting people using Yolo-OpenCV
Yolo object detection in pytorch
Another Yolo implementation in Pytorch
Similar question on ai.stackexchange
You can also utilize frameworks such as Detectorn2, mmdetection for these tasks.(Or Tensorflow's ObjectDetectionAPI , ect)
I am new in Pytorch. My question is: How do I apply transfer learning to a custom dataset? I am doing image segmentation on brain tumors. I can find examples which use U-net structure but I could not find examples using weights of the pre-trained models for a U-net image segmentation?
You could obtain pre-trained models in two ways:
Model weights or complete models shared in formats such .pt or .pth:
In this case, Saving and Loading Models is a good starting point. Copying from the tutorial there, you could load a model as
model = TheModelClass(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
The other way is to load the model from torchvision. A list is available models is available at Torchvision Models. U-Net is not available yet. However, it is possible to load a pre-trained model as the encoder and write a separate decoder to form a U-Net with a pre-trained encoder.
In this case, the model object returned from the function calls shown in the API are already loaded with pretrained weights when pretrained=True.
For writing a custom dataloader, PyTorch data loaders may be a useful guide.
I have h5 weights from a Keras model.
I want to rewrite the Keras model into a tf.keras model (using TF2.x).
I know that only the high level API changed, but do you know if I still can use the h5 weights?
Most likely they can be loaded, but is the structure different between Keras and tf.keras weights?
Thanks
It seems that they are the same
cudos to Mohsin hasan answer
In the past, when I had to convert tf.keras model to keras model, I
did following:
Train model in tf.keras
Save only the weights tf_model.save_weights("tf_model.hdf5")
Make Keras model architecture using all layers in keras (same as the tf keras one)
load weights by layer names in keras: keras_model.load_weights(by_name=True)
This seemed to work for me. Since, I was using out of box architecture
(DenseNet169), I had to very less work to replicate tf.keras network
to keras.
And the answer from Alex Cohn
tf.keras HDF5 model and Keras HDF5 models are not different things,
except for inevitable software version update synchronicity. This is
what the official docs say:
tf.keras is TensorFlow's implementation of the Keras API specification. This is a high-level API to build and train models that
includes first-class support for TensorFlow-specific functionality
If the convertor can convert a keras model to tf.lite, it will deliver
same results. But tf.lite functionality is more limited than tf.keras.
If this feature set is not enough for you, you can still work with
tensorflow, and enjoy its other advantages.
I am using Pytorch for image classification. I am looking for CNN models pretrained on a dataset other than ImageNet, I have found a link to a ".ckpt" file. I also found tutorials on loading this file with Tenserflow, but not using pytorch.
How can I load pretrained model using Pytorch from ".ckpt" file ?
I agree with #jodag that in general, PyTorch and Tensorflow are not interoperable. There are some special cases in which you may be able to do this. For example, HuggingFace provides support for converting the transformer model from TensorFlow to PyTorch.
There is a related (though closed) question on DataScience StackExchange where the idea is to rewrite the Tensorflow model into PyTorch and then loads the weights from the checkpoint file. Note that this can get tricky at times.
I was exploring turicreate ObjectDetector API. The documentation mentions it is a trained model. I wanted to know if I can use this trained model & detect the 1000 labels which was used to originally train this turi model. All the examples mention to train with our dataset, I do not want to train but wanted to use pre-trained model which can classify. Any help is appreciated.
Is your question about how to load and use a pre-trained model? Turi create API docs mentions a load_model method:
model.save('my_model_file')
loaded_model = tc.load_model('my_model_file')
EDIT: Yep, ObjectDetector exposes a save method that works well with load_model.