After I train my object detector using the Tensorflow object detection API(to detect only cars).
I get an mAP value around 0.32 while running the eval.py script.
However, in the Tensorflow Detection model Zoo page(which can be accessed at https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md), the mAP's for various models are 100 times the value I have obtained but when I look at my model's predictions on the test set, they are actually pretty good.
Should I multiply the value which I have obtained by 100 or is there something wrong with the model that I have trained?
Related
I am implementing YOLOv3 and have trained the model on my custom class ( which is tomato). I have used the darknet model 53 weights ( https://pjreddie.com/media/files/darknet53.conv.74) to start my training as per the instructions provided by many sites on training and object detection using YOLOv3 . I thought it was not necessary to list down the steps.
One of my object images used for training is shown below ( with bounding boxes using LabelImg):
The txt file for the above image for the bounding boxes contains the following coordinates , as created using labellmg:
0 0.152807 0.696655 0.300640 0.557093
0 0.468728 0.705306 0.341862 0.539792
0 0.819652 0.695213 0.337242 0.543829
0 0.317164 0.271626 0.324449 0.501730
Now when I use the same image for testing to determine the accuracy of detection, it is unable to detect all the tomatoes and moreover the bounding boxes are way off from the objects as shown below:
I am not sure what is going on.
I have cloned the git
https://github.com/AlexeyAB/darknet and did a local make and trained the model on the custom object. Nothing fancy.
The pictures above were taken from my phone. I have trained the darknet using a combination of downloaded images and custom tomato pictures I had taken from my phone. I have 290 images for training.
Maybe your model can't generalize well. Maybe your are training too much, which can cause over-fitting or even your dataset is small.
You can try testing on a never seen data (a new tomato picture) and sees if it does well.
Double-check your config files, if something is incorrect there, like you are using a yolov4 cfg in a yolov3 model.
And I recommend that you read this article in which can help you understand better how neural networks works:
https://towardsdatascience.com/understand-neural-networks-model-generalization-7baddf1c48ca
I usually get to feature importance using
regr = XGBClassifier()
regr.fit(X, y)
regr.feature_importances_
where type(regr) is .
However, I have a pickled mXGBoost model, which when unpacked returns an object of type . This is the same object as if I would have ran regr.get_booster().
I have found a few solutions for getting variable importance from a booster object, but is there a way to get to the classifier object from the booster object so I can just apply the same feature_importances_ command? This seems like the most straightforward solution, or it seems like I have to write a function that mimics the output of feature_importances_ in order for it to fit my logged feature importances...
So ideally I'd have something like
xbg_booster = pickle.load(open("xgboost-model", "rb"))
assert str(type(xgb_booster)) == "<class 'xgboost.core.Booster'>", 'wrong class'
xgb_classifier = xgb_booster.get_classifier()
xgb_classifier.feature_importances_
Are there any limitations to what can be done with a booster object in terms finding the classifier? I figure there's some combination of save/load/dump that will get me what I need but I'm stuck for now...
Also for context, the pickled model is the output from AWS sagemaker, so I'm just unpacking it to do some further evaluation
Based on my own experience trying to recreate a classifier from a booster object generated by SageMaker I learned the following:
It doesn't appear to be possible to recreate the classifier from the booster. :(
https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster has the details on the booster class so you can review what it can do.
Crazy things you can do however:
You can create a classifier object and then over-ride the booster within it:
xgb_classifier = xgb.XGBClassifier(**xgboost_params)
[..]
xgb_classifier._Boster = booster
This is nearly useless unless you fit it otherwise it doesn't have any feature data. (I didn't go all the way through this scenario to validate if fitting would provide the feature data required to be functional.)
You can remove the booster object from the classifier and then pickle the classifier using xgboost directly. Then later restore the SageMaker booster back into it. This abomination is closer and appears to work, but is not truly a rehydrated classifier object from the SageMaker output alone.
Recommendation
If you’re not stuck using the SageMaker training solution you can certainly use XGBoost directly to train with. At that point you have access to everything you need to dump/save the data for use in a different context.
I know you're after feature importance so I hope this gets you closer, I had a different use case and was ultimately able to leverage the booster for what I needed.
I was able to get xgboost.XGBClassifier model virtually identical to a xgboost.Booster version model by
(1) extracting all tuning parameters from the booster model using this:
import json
json.loads(your_booster_model.save_config())
(2) implementing these same tuning parameters and then training a XGBClassifier model using the same training dataset used to train the Booster model before that.
Note: one mistake I made was that I forgot to explicitly assign the same seed /random_state in both Booster and Classifier versions.
Keras 2.0 removed F1 score, but I would like to monitor its value. I am using a sequential model to train a Neural Net.
I defined a function, as suggested here How to calculate F1 Macro in Keras?.
This function works fine only if used it inside model.compile. In this way I see its value at each step. The problem is that I don't want just to see its value but I would like my training to behave differently according to its value, using the callbacks of Keras.
If I try to insert my custom metric in the callbacks then I get this error:
'function object is not iterable'
Do you know how to define a function such that it can be used as an argument in the callbacks?
Callback of Keras will enable us to retrieve the model at different period, based on the metric which we keep track of. This will not affect the training procedure of the model.
You can train your model only with respect to some loss function. For example, cross entropy for classification problem. The readily available loss function in keras are given here
Precision, recall or f1-score are not differentialable functions. Hence, we cannot use that as a loss function for model training.
May be, if you want to tune your hyperparameter (such as learning rate, class weights) for improving f1 score, then you can be do that.
For tuning hyper parameters you can use hyperopt, tutorials
I have implemented a custom metric based on SIM and when i try the code it works. I have implemented it using tensors and np arrays and both give the same results. However when I start fitting the model the values given back are a lot higher then the values I get when i load the weights generated by the training and applying the same function.
My function is:
def SIM(y_true,y_pred):
n_y_true=y_true/(K.sum(y_true)+K.epsilon())
n_y_pred=y_pred/(K.sum(y_pred)+K.epsilon())
return K.mean(K.sum( K.minimum(n_y_true, n_y_pred)))
When I compile the Keras model I add this to the metrics and during training it gives for example SIM: 0.7092.
When i load the weights and try it the SIM score is around 0.3. The correct weights are loaded (when restarting training with these weights the same values popup). Does anybody know if I am doing anything wrong?
Why are the metrics given back during training so much higher compared to running the function over a batch?
I've implemented a neural network using Keras. Once trained and tested for final test accuracy, using a matrix with a bunch of rows containing features (plus corresponding labels), I have a model which I should be able to use for prediction.
How can I feed a single unseen example, meaning a feature vector to the model, to obtain a class prediction?
I've looked at their documentation here but could not find a method for it.
What you want is the predict method, it takes a batch of input samples and produces predictions, which are the outputs computer by your network. To feed a single example you can just put it inside a numpy ndarray wrapper.