I have a pretrained CNN (Resnet-18) trained on Imagenet, now i want to extend it on my own dataset of video frames , now the point is all tutorials i found on Finetuning required dataset to be organised in classes like
class1/train/
class1/test/
class2/train/
class2/test/
but i have only frames on many videos , how will i train my CNN on it.
So can anyone point me in right direction , any tutorial or paper etc ?
PS: My final task is to get deep features of all frames that i provide at the time of testing
for training network, you should have some 'label'(sometimes called y) of your input data. from there, network calculate loss between logit(answer of network) and the given label.
And the network will self-revise using that loss value by backpropagating. that process is what we call 'training'.
Because you only have input data, not label, so you can get the logit only. that means a loss cannot be calculated.
Fine tuning is almost same word with 'additional training', so that you cannot fine tuning your pre-trained network without labeled data.
About train set & test set, that is not the problem right now.
If you have enough labeled input data, you can divide it with some ratio.
(e.g. 80% of data for training, 20% of data for testing)
the reason why divide data into these two sets, we want to check the performance of our trained network more general, unseen situation.
However, if you just input your data into pre-trained network(encoder part), it will give a deep feature. It doesn't exactly fit to your task, still it is deep feature.
Added)
Unsupervised pre-training for convolutional neural network in theano
here is the method you need, deep feature encoder in unsupervised situation. I hope it will help.
Related
I have a dataset composed of 10k-15k pictures for supervised object detection which is very different from Imagenet or Coco (pictures are much darker and represent completely different things, industrial related).
The model currently used is a FasterRCNN which extracts features with a Resnet used as a backbone.
Could train the backbone of the model from scratch in one stage and then train the whole network in another stage be beneficial for the task, instead of loading the network pretrained on Coco and then retraining all the layers of the whole network in a single stage?
From my experience, here are some important points:
your train set is not big enough to train the detector from scratch (though depends on network configuration, fasterrcnn+resnet18 can work). Better to use a pre-trained network on the imagenet;
the domain the network was pre-trained on is not really that important. The network, especially the big one, need to learn all those arches, circles, and other primitive figures in order to use the knowledge for detecting more complex objects;
the brightness of your train images can be important but is not something to stop you from using a pre-trained network;
training from scratch requires much more epochs and much more data. The longer the training is the more complex should be your LR control algorithm. At a minimum, it should not be constant and change the LR based on the cumulative loss. and the initial settings depend on multiple factors, such as network size, augmentations, and the number of epochs;
I played a lot with fasterrcnn+resnet (various number of layers) and the other networks. I recommend you to use maskcnn instead of fasterrcnn. Just command it not to use the masks and not to do the segmentation. I don't know why but it gives much better results.
don't spend your time on mobilenet, with your train set size you will not be able to train it with some reasonable AP and AR. Start with maskrcnn+resnet18 backbone.
I am currently working on a project in which I need to use RNNs in part of a neural network. Essentially the RNN would take in a text of variable length and output a feature representation. This feature representation would then be clubbed with some more feature vectors and fed to a different graph neural network. Some loss would be calculated on the output of the graph neural network and this loss would be backpropagated across the entire network including the RNN and be used to train the entire end to end network.
However, I am not able to wrap my head around how I can use the RNN as a part of another different non-sequential model. I use PyTorch for most of my work.
Can anyone suggest any way in which I may address this problem. Or refer to any material which might be useful.
Thanks
I have a small dataset collect from imagenet(7 classes each class with 1000 training data). I try to train it with alexnet model. But somehow the accuracy just cant go any higher(about 68% maximum). I remove conv4 and conv5 layer to prevent model overfitting also decrease the number of neuron in each layer(conv and fc). here is my setup.
Did i do anything wrong so that the accuracy is so low?
I want to sort out a few terms:
(1) A perceptron is an individual cell in a neural net.
(2) In a CNN, we generally focus on the kernel (filter) as a unit; this is the square matrix of perceptrons that forms a psuedo-visual unit.
(3) The only place it usually makes sense to focus on an individual perceptron is in the FC layers. When you talk about removing some of the perceptrons, I think you mean kernels.
The most important part of training a model is to make sure that your model is properly fitted to the problem at hand. AlexNet (and CaffeNet, the BVLC implementation) is fitted to the full ImageNet data set. Alex Krizhevsky and his colleagues spent a lot of research effort in tuning their network to the problem. You are not going to get similar accuracy -- on a severely reduced data set -- by simply removing layers and kernels at random.
I suggested that you start from CONVNET (the CIFAR-10 net) because it's much better tuned to this scale of problem. Most of all, I strongly recommend that you make constant use of your visualization tools, so that you can detect when the various kernel layers begin to learn their patterns, and to see the effects of small changes in the topology.
You need to run some experiments to tune and understand your topology. Record the kernel visualizations at chosen times during the training -- perhaps at intervals of 10% of expected convergence -- and compare the visual acuity as you remove a few kernels, or delete an entire layer, or whatever else you choose.
For instance, I expect that if you do this with your current amputated CaffeNet, you'll find that the severe losses in depth and breadth greatly change the feature recognition it's learning. The current depth of building blocks is not enough to recognize edges, then shapes, then full body parts. However, I could be wrong -- you do have three remaining layers. That's why I asked you to post the visualizations you got, to compare with published AlexNet features.
edit: CIFAR VISUALIZATION
CIFAR is much better differentiated between classes than is ILSVRC-2012. Thus, the training requires less detail per layer and fewer layers. Training is faster, and the filters are not nearly as interesting to the human eye. This is not a problem with the Gabor (not Garbor) filter; it's just that the model doesn't have to learn so many details.
For instance, for CONVNET to discriminate between a jonquil and a jet, we just need a smudge of yellow inside a smudge of white (the flower). For AlexNet to tell a jonquil from a cymbidium orchid, the network needs to learn about petal count or shape.
I have a dataset of around 6K chemical formulas which I am preprocessing via Keras' tokenization to perform binary classification. I am currently using a 1D convolutional neural network with dropouts and am obtaining an accuracy of 82% and validation accuracy of 80% after only two epochs. No matter what I try, the model just plateaus there and doesn't seem to be improving at all. Those same exact accuracies are reached with a vanilla LSTM too. What else can I try to improve my accuracies? Losses only have a difference of 0.04... Anyone have any ideas? Both models use an embedding layer and changing the output dimension isn't having an effect either.
According to your answer, I believe your model has a high bias and low variance (see this link for further details). Thus, your model is not fitting your data very well and it is causing underfitting. So, I suggest you 3 things:
Train your model a little longer: I believe two epoch are too few to give a chance to your model understand the patterns in the data. Try to minimize learning rate and increase the number of epochs.
Try a different architecture: you may change the amount of convolutions, filters and layers, You can also use different activation functions and other layers like max pooling.
Make an error analysis: once you finished your training, apply your model to test set and take a look into the errors. How much false positives and false negatives do you have? Is your model better to classify one class than the other? You can see a pattern in the errors that may be related to your data?
Finally, if none of these suggestions helped you, you may also try to increase the number of features, if possible.
I trained GoogLeNet model from scratch. But it didn't give me the promising results.
As an alternative, I would like to do fine tuning of GoogLeNet model on my dataset. Does anyone know what are the steps should I follow?
Assuming you are trying to do image classification. These should be the steps for finetuning a model:
1. Classification layer
The original classification layer "loss3/classifier" outputs predictions for 1000 classes (it's mum_output is set to 1000). You'll need to replace it with a new layer with appropriate num_output. Replacing the classification layer:
Change layer's name (so that when you read the original weights from caffemodel file there will be no conflict with the weights of this layer).
Change num_output to the right number of output classes you are trying to predict.
Note that you need to change ALL classification layers. Usually there is only one, but GoogLeNet happens to have three: "loss1/classifier", "loss2/classifier" and "loss3/classifier".
2. Data
You need to make a new training dataset with the new labels you want to fine tune to. See, for example, this post on how to make an lmdb dataset.
3. How extensive a finetuning you want?
When finetuning a model, you can train ALL model's weights or choose to fix some weights (usually filters of the lower/deeper layers) and train only the weights of the top-most layers. This choice is up to you and it ususally depends on the amount of training data available (the more examples you have the more weights you can afford to finetune).
Each layer (that holds trainable parameters) has param { lr_mult: XX }. This coefficient determines how susceptible these weights to SGD updates. Setting param { lr_mult: 0 } means you FIX the weights of this layer and they will not be changed during the training process.
Edit your train_val.prototxt accordingly.
4. Run caffe
Run caffe train but supply it with caffemodel weights as an initial weights:
~$ $CAFFE_ROOT/build/tools/caffe train -solver /path/to/solver.ptototxt -weights /path/to/orig_googlenet_weights.caffemodel
Fine-tuning is a very useful trick to achieve a promising accuracy compared to past manual feature. #Shai already posted a good tutorial for fine-tuning the Googlenet using Caffe, so I just want to give some recommends and tricks for fine-tuning for general cases.
In most of time, we face a task classification problem that new dataset (e.g. Oxford 102 flower dataset or Cat&Dog) has following four common situations CS231n:
New dataset is small and similar to original dataset.
New dataset is small but is different to original dataset (Most common cases)
New dataset is large and similar to original dataset.
New dataset is large but is different to original dataset.
In practice, most of time we do not have enough data to train the network from scratch, but may be enough for pre-trained model. Whatever which cases I mentions above only thing we must care about is that do we have enough data to train the CNN?
If yes, we can train the CNN from scratch. However, in practice it is still beneficial to initialize the weight from pre-trained model.
If no, we need to check whether data is very different from original datasets? If it is very similar, we can just fine-tune the fully connected neural network or fine-tune with SVM. However, If it is very different from original dataset, we may need to fine-tune the convolutional neural network to improve the generalization.