Buoy | An extension to the built in Android Battery Saver

A little eery that I made it to this page searching for a solution to the same problem, also while trying to scrape King County info. I'll keep hunting and will post back if I find something useful.

The option of std::vector looks pretty good. You don't need to prevent data from modifying, just use const after creation.

If you need fast access to rows by location, temperature, etc. you may create specific indices based on map or more complex data structures.

For example, you may create std::multimap where the key is a temperature and the value is a number of row in the table (index in vector). Then, you can use std::lower_bound and std::upper_bound to retrieve all indices of rows with a temperature in a given range.

The mAP for object detection is the average of the AP calculated for all the classes. mAP@0.5 means that it is the mAP calculated at IOU threshold 0.5.

The general definition for the Average Precision(AP) is finding the area under the precision-recall curve.

The process of plotting the model's precision and recall as a function of the model’s confidence threshold is the precision recall curve.

Precision measures how accurate is your predictions. i.e. the percentage of your predictions that are correct. Recall measures how good you find all the positives. F1 score is HM (Harmonic Mean) of precision and recall.

To answer your questions now.

How to read it and which model is really better in this case?

• The mAP is a good measure of the sensitivity of the neural network. So good mAP indicates a model that's stable and consistent across difference confidence thresholds. In your case faster rcnn results indicate that precision-recall curve metric is bad compared to that of Yolov3, which means that either faster rcnn has very bad recall at higher confidence thresholds or very bad precision at lower confidence threshold compared to that of Yolov3 (especially for small objects).
• Precision, Recall and F1 score are computed for given confidence threshold. I'm assuming you're running the model with default confidence threshold (could be 0.25). So higher Precision, Recall and F1 score of faster rcnn indicate that at that confidence threshold it's better in terms of all the 3 metric compared to that of Yolov3.

What metric should be more important?

In general to analyse better performing model, I would suggest you to use validation set (data set that is used to tune hyper-parameters) and test set (data set that is used to assess the performance of a fully-trained model).

Note: FP - False Positive FN - False Negative

On validation set:

• Use mAP to select best performing model (model that is more stable and consistent) out of all the trained weights across iterations/epochs. Use mAP to understand whether model should be trained/tuned further or not.

• Check class level AP values to ensure model is stable and good across the classes.

• As per use-case/application, if you're completely tolerant to FNs and highly intolerant to FPs then to train/tune the model accordingly use Precision.

• As per use-case/application, if you're completely tolerant to FPs and highly intolerant to FNs then to train/tune the model accordingly use Recall.

On test set:

• If you're neutral towards FPs and FNs, then use F1 score to evaluate best performing model.

• If FPs are not acceptable to you (without caring much about FNs) pick the model with higher Precision

• If FNs are not acceptable to you (without caring much about FPs) pick the model with higher Recall

• Once you decide metric you should be using, try out multiple confidence thresholds (say for example - 0.25, 0.35 and 0.5) for given model to understand for which confidence threshold value the metric you selected works in your favour and also to understand acceptable trade off ranges (say you want Precision of at least 80% and some decent Recall). Once confidence threshold is decided, you use it across different models to find out best performing model.

The BuoyClient.showFloatWindow interface needs to be called in the onResume of all game interface Activity to display the buoy. Click to view more: https://developer.huawei.com/consumer/en/doc/development/HMS-Guides/game-buoy-v4#h1-1576589973315

No it would not. There is a different parameter pad_to_max_length which you have to set to True to add padding tokens. add_special_tokens will add the [CLS] and the [SEP] token (101 and 102 respectively).

Your error description is not very precise, but I assume that the problem is that you are not adding the class (string), but its index.

Try replacing

Because the nav has a fixed position, while the main has a static position.

Quoting from W3schools: "A fixed element does not leave a gap in the page where it would normally have been located."

To overcome this, you might need to give a top-margin for the main section by the same value of the nav height.

You should load the google maps script before your script.

Because google.maps.Map is a function constructor and you instantiate using the new keyword, it must be defined before you can use it. And you should also move your script at the bottom or use

You can try to use float property:

https://www.w3schools.com/css/css_float.asp