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i'm beginner in this field and i try to use roboflow to labeling and i found that available credit for training is 2. what that means?
i'm try to training my dataset but i'm worried that I can only do 2 trainings
new workspaces come with 3 training credits to start. If you have 2, you’ve either used a credit to train a model with Roboflow Train, or hadn’t trained one before, so the first time you exported, a model was auto-trained for you in the background to give you a Label Assist checkpoint and access to the Deploy Tab.
https://docs.roboflow.com/annotate/model-assisted-labeling
https://blog.roboflow.com/deploy-tab
Credits apply for Roboflow Train, the AutoML training from Roboflow.
training documentation: https://docs.roboflow.com/train
If you’re training custom or open-source model architectures, such as Detectron2, YOLOv7, YOLOv8, etc., you don’t use credits. Custom or open-source model training doesn’t require any credits.
custom models: https://github.com/roboflow/notebooks
Weights uploads from custom models (YOLOv5 and YOLOv8 object detection, YOLOv7 instance segmentation currently supported): https://docs.roboflow.com/upload-weights
In short, you can train 2 more times with Roboflow Train, and as many times as you want for custom models so long as your source and generated image limits are not exceeded for the workspace.
Here’s how to ask for workspace upgrades such as paid plans, or sponsorships for upgrades: https://docs.roboflow.com/roboflow-workspaces#workspace-upgrades
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The basic process for most supervised machine learning problems is to divide the dataset into a training set and test set and then train a model on the training set and evaluate its performance on the test set. But in many (most) settings, disease diagnosis for example, more data will be available in the future. How can I use this to improve upon the model? Do I need to retrain from scratch? When might be the appropriate time to retrain if this is the case (e.g., a specific percent of additional data points)?
Let’s take the example of predicting house prices. House prices change all the time. The data you used to train a machine learning model that predicts house prices six months ago could provide terrible predictions today. For house prices, it’s imperative that you have up-to-date information to train your models.
When designing a machine learning system it is important to understand how your data is going to change over time. A well-architected system should take this into account, and a plan should be put in place for keeping your models updated.
Manual retraining
One way to maintain models with fresh data is to train and deploy your models using the same process you used to build your models in the first place. As you can imagine this process can be time-consuming. How often do you retrain your models? Weekly? Daily? There is a balance between cost and benefit. Costs in model retraining include:
Computational Costs
Labor Costs
Implementation Costs
On the other hand, as you are manually retraining your models you may discover a new algorithm or a different set of features that provide improved accuracy.
Continuous learning
Another way to keep your models up-to-date is to have an automated system to continuously evaluate and retrain your models. This type of system is often referred to as continuous learning, and may look something like this:
Save new training data as you receive it. For example, if you are receiving updated prices of houses on the market, save that information to a database.
When you have enough new data, test its accuracy against your machine learning model.
If you see the accuracy of your model degrading over time, use the new data, or a combination of the new data and old training data to build and deploy a new model.
The benefit to a continuous learning system is that it can be completely automated.
At first, i want to binary classify "Fire" event in 5000 images. Secondly, if fire is available in images, then classify further either its urban area(buildings) or rural (As forest). I am using Transfer learning with different models including VGG16 and fine-tune few of its last layers.
I have already tried by training and testing both classification steps separately, but it creates lot of penalty, if i identify that image has fire in rural area but image does not include fire.
I want transfer learning model to binary classify and produce results of both steps as:
img1 fire rural/urban
1 No-fire No-rural/no-urban
2 Fire urban
3 Fire rural
so can i retrain vgg16 in a way to it provides both level of classification i-e step one (fire/no-fire) and step two (rural/urban)
I have found the answer, the same model will be trained to binary classify availability of fire and its weights will be saved.
On next step, same weights can be used for second step, to identify either fire is in rural areas or urban areas. Thanks
I'm trying to utilize a pre-trained model like Inception v3 (trained on the 2012 ImageNet data set) and expand it in several missing categories.
I have TensorFlow built from source with CUDA on Ubuntu 14.04, and the examples like transfer learning on flowers are working great. However, the flowers example strips away the final layer and removes all 1,000 existing categories, which means it can now identify 5 species of flowers, but can no longer identify pandas, for example. https://www.tensorflow.org/versions/r0.8/how_tos/image_retraining/index.html
How can I add the 5 flower categories to the existing 1,000 categories from ImageNet (and add training for those 5 new flower categories) so that I have 1,005 categories that a test image can be classified as? In other words, be able to identify both those pandas and sunflowers?
I understand one option would be to download the entire ImageNet training set and the flowers example set and to train from scratch, but given my current computing power, it would take a very long time, and wouldn't allow me to add, say, 100 more categories down the line.
One idea I had was to set the parameter fine_tune to false when retraining with the 5 flower categories so that the final layer is not stripped: https://github.com/tensorflow/models/blob/master/inception/README.md#how-to-retrain-a-trained-model-on-the-flowers-data , but I'm not sure how to proceed, and not sure if that would even result in a valid model with 1,005 categories. Thanks for your thoughts.
After much learning and working in deep learning professionally for a few years now, here is a more complete answer:
The best way to add categories to an existing models (e.g. Inception trained on the Imagenet LSVRC 1000-class dataset) would be to perform transfer learning on a pre-trained model.
If you are just trying to adapt the model to your own data set (e.g. 100 different kinds of automobiles), simply perform retraining/fine tuning by following the myriad online tutorials for transfer learning, including the official one for Tensorflow.
While the resulting model can potentially have good performance, please keep in mind that the tutorial classifier code is highly un-optimized (perhaps intentionally) and you can increase performance by several times by deploying to production or just improving their code.
However, if you're trying to build a general purpose classifier that includes the default LSVRC data set (1000 categories of everyday images) and expand that to include your own additional categories, you'll need to have access to the existing 1000 LSVRC images and append your own data set to that set. You can download the Imagenet dataset online, but access is getting spotier as time rolls on. In many cases, the images are also highly outdated (check out the images for computers or phones for a trip down memory lane).
Once you have that LSVRC dataset, perform transfer learning as above but including the 1000 default categories along with your own images. For your own images, a minimum of 100 appropriate images per category is generally recommended (the more the better), and you can get better results if you enable distortions (but this will dramatically increase retraining time, especially if you don't have a GPU enabled as the bottleneck files cannot be reused for each distortion; personally I think this is pretty lame and there's no reason why distortions couldn't also be cached as a bottleneck file, but that's a different discussion and can be added to your code manually).
Using these methods and incorporating error analysis, we've trained general purpose classifiers on 4000+ categories to state-of-the-art accuracy and deployed them on tens of millions of images. We've since moved on to proprietary model design to overcome existing model limitations, but transfer learning is a highly legitimate way to get good results and has even made its way to natural language processing via BERT and other designs.
Hopefully, this helps.
Unfortunately, you cannot add categories to an existing graph; you'll basically have to save a checkpoint and train that graph from that checkpoint onward.
After running the Machine Learner Algorithm (SVM) on training data using GATE tool, I would like to test it on testing data. My question is, should I use the same trained data to be tested, also, how could the model extract the entities from the test data while the test data not annotated with the annotations that have been learnt in the trained data.
I followed the tutorial on this link http://gate.ac.uk/sale/talks/gate-course-may11/track-3/module-11-machine-learning/module-11.pdf but at the end it was a bit confusing when it talks about splitting the dataset into training and testing.
In GATE you have 3 modes of the machine learning PR - for training, evaluation and application.
What happens when you train is that the ML PR is checking the selected annotation (let's say Token), collecting it's features and learning the target class (i.e. Person, Mention or whatever). Using the example docs, the ML PR creates a model which holds values for features and basically "learns" how to classify new Tokens (or sentences, or other).
When testing, you provide the ML PR only the Tokens with all their features. Then the ML PR uses them as input for its model and decides if or what Mention to create. The ML PR actually needs everything that was there in the training corpus, except the label / target class / mention - the decision that should be made.
I think the GATE ML PR ignores the labels when in test mode, so it's not crucial to remove it.
Evaluation is a helpful option, where training and testing are done automatically, the corpus is split and results are presented. What it does is split the corpus in 2, train on one part, apply the model on the other, compare the gold standard to what it labeled. Repeat with different splits.
The usual sequence is to train and evaluate, check results, fix, add features, etc. and when you're happy with the evaluation results, switch to application and run on data that doesn't have labels.
It is crucial that you run the same pre-processing when you're training and testing. For instance if in training you've run a POS tagger and you skip this when testing, the ML PR won't have the "Token.category" feature and will calculate very different results.
Now to your questions :)
NO! Don't use the same data for testing, that is a very common mistake, if you get suspiciously good results, first check if you're doing that.
In the tutorial, when you split the corpus both parts will have all the annotations as before, so the ML PR will have all the features it needed. In real life, you'll have to run some pre-processing first as docs will come without tokens or anything.
Splitting in their case is done very simple - just save all docs to files, split files in two folders, load them as two corpora.
Hope this helps :)
If I have a video dataset of a specific action , how could I use them to train a classifier that could be used later to classify this action.
The question is very generic. In general, there is no foul proof way of training a classifier that will work for everything. It highly depends on the data you are working with.
Here is the 'generic' pipeline:
extract features from the video
label your features (positive for the action you are looking for; negative otherwise)
split your data into 2 (or 3) sets. One for training, one for testing and the other optionally for validation
train a classifier on the labeled examples (e.g. SVM, Neural Network, Nearest Neighbor ...)
validate the results on the validation data, if that is appropriate for the algorithm
test on data you haven't used for training.
You can start with some machine learning tools here http://www.cs.waikato.ac.nz/ml/weka/
Make sure you never touch the test data for any other purposes than testing
Good luck
Almost 10 years later, here's an updated answer.
Set up a camera and collect raw video data
Save it somewhere in form of single frames. Do this yourself locally or using a cloud bucket or use a service like Sieve API. Helpful repo linked here.
Export from Sieve or cloud bucket to get data labeled. Do this yourself or using some service like Scale Rapid.
Split your dataset into train, test, and validation.
Train a classifier on the labeled samples. Use transfer learning over some existing model and fine-tune just the last few layers.
Run your model over the test set after each training epoch and save the one with the best test set performance.
Evaluate your model at the end using the validation set.
There are many repos that can help you get started: https://github.com/weiaicunzai/awesome-image-classification
The two things that can help you ensure best results include 1. high quality labeled data and 2. a diverse, curated dataset. That's what Sieve can help with!