I want to extract features using caffe and train those features using SVM. I have gone through this link: http://caffe.berkeleyvision.org/gathered/examples/feature_extraction.html. This links provides how we can extract features using caffenet. But I want to use Lenet architecture here. I am unable to change this line of command for Lenet:
./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 leveldb
And also, after extracting the features, how to train these features using SVM? I want to use python for this. For eg: If I get features from this code:
features = net.blobs['pool2'].data.copy()
Then, how can I train these features using SVM by defining my own classes?
You have two questions here:
Extracting features using LeNet
Training an SVM
Extracting features using LeNet
To extract the features from LeNet using the extract_features.bin script you need to have the model file (.caffemodel) and the model definition for testing (.prototxt).
The signature of extract_features.bin is here:
Usage: extract_features pretrained_net_param feature_extraction_proto_file extract_feature_blob_name1[,name2,...] save_feature_dataset_name1[,name2,...] num_mini_batches db_type [CPU/GPU] [DEVICE_ID=0]
So if you take as an example val prototxt file this one (https://github.com/BVLC/caffe/blob/master/models/bvlc_alexnet/train_val.prototxt), you can change it to the LeNet architecture and point it to your LMDB / LevelDB. That should get you most of the way there. Once you did that and get stuck, you can re-update your question or post a comment here so we can help.
Training SVM on top of features
I highly recommend using Python's scikit-learn for training an SVM from the features. It is super easy to get started, including reading in features saved from Caffe's format.
Very lagged reply, but should help.
Not 100% what you want, but I have used the VGG-16 net to extract face features using caffe and perform a accuracy test on a small subset of the LFW dataset. Exactly what you needed is in the code. The code creates classes for training and testing and pushes them into the SVM for classification.
https://github.com/wajihullahbaig/VGGFaceMatching
Related
I've managed to save a model for automatic translation with pytorch and I'm wondering how can I use it to translate input sentences.
I came across an article suggesting to use a class_mapping so I guess that I should use the dictionary available on that github page as python list https://github.com/facebookresearch/fairseq/tree/nllb
I used this line of code to load the model :
model = torch.load(PATH)
model.eval()
So my question is how can I use the model and the dictionary in order to "predict" the translation of a sentence ?
Thank you
I'm using Windows 10 machine. Libraries: Keras with Tensorflow 2.0 Embeddings: Glove(100 dimensions).
I am trying to implement an LSTM architecture for multi-label text classification.
I am using different types of fine-tuning to achieve better results but with no luck so far.
The main problem I believe is the difference in class distributions of my dataset but after a lot of tries and errors, I couldn't implement stratified-k-split in Keras.
I am also experimenting with dropout layers, batch sizes, # of layers, learning rates, clip values, validation splits but I get a minimum boost or worst performance sometimes.
For metrics, I use mainly ROC and F1.
I also followed the suggestion from a StackOverflow member who said to delete some of my examples so I can balance my dataset but if I do that I will have a very low number of examples.
What would you suggest to me?
If someone can provide code based on my implementation for
stratified-k-split I would be grateful cause I have checked all the
online resources but can't implement it.
Any tips, suggestions will be really helpful.
Metrics Plots
Dataset form+Embedings form+train-test-split form
Dataset's labels distribution
My LSTM implementation
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)
According to the comment at the top of the TextCategorizer,
Train a convolutional neural network text classifier on the IMDB
dataset, using the TextCategorizer component. The dataset will be
loaded automatically via Thinc's built-in dataset loader. The model is
added to spacy.pipeline, and predictions are available via doc.cats.
For more details, see the documentation:
* Training: https://spacy.io/usage/training
Where is the code for the CNN? Can the CNN be configured? Is there a research paper the implementation is based on?
The network architecture is defined in the _.ml module specifically within the build_text_classifier function.
The code related with the training is within the pipeline module specifically within the TextCategorizer class.
Some parameters like drop_out, batch_size and the number of epochs can be configured as showed in the example, you can also modify the architecture of the network but for that you have to know about the framework behind spaCy which is called Thinc https://github.com/explosion/thinc and some Cython.
I don't know about any paper describing the model but this video provide a great description of it https://www.youtube.com/watch?v=sqDHBH9IjRU
I'm new in the field of Deep Neural Network. There are various deep learning frameworks nearby. Notably Theano, Torch7, Caffe, and recently open sourced TensorFlow. I have tried out a couple of tutorials with TensorFlow provided on their site. Specifically the MNIST dataset. I guess this is the hello world of every deep learning framework out there. I also viewed tutorials from here. This one was explained in detail, but they do not provide hands on experience with any deep learning frameworks. So which framework should be better for beginners? I looked up similar questions asked on Quora. Some said that theano is tougher to learn but it gives more control, Caffe is easier, but it gives less control over the network. And nothing on Tensorflow, as it is new, but from what i've seen the documentation is not That well written, also it seems tougher to understand. So as a newbie what should i choose to learn?
Another question, As I said, MNIST is the hello world of every deep learning framework, and many neural networks can be found for recognizing MNIST dataset. So, if I use the same network to detect other dataset, say CIFAR-10 dataset, will it work?? Let's just say that i turn the CIFAR-10 dataset to grayscale images and convert it to same dimension as MNIST dataset. Will the model be invalid or fail to learn? or have bad accuracy or what?