kandi X-RAY | myDrive Summary
kandi X-RAY | myDrive Summary
MyDrive is an Open Source cloud file storage server (Similar To Google Drive). Host myDrive on your own server or trusted platform and then access myDrive through your web browser. MyDrive uses mongoDB to store file/folder metadata, and supports multiple databases to store the file chunks, such as Amazon S3, the Filesystem, or just MongoDB. MyDrive is built using Node.js, and Typescript. The service now even supports Docker images!. Go to the main myDrive website for more infomation, screenshots, and more.
Top functions reviewed by kandi - BETA
- Detects a file for the specified document .
- Calculate the MD5 hash .
- This function is used to detect conflicts within a browser . It s used to detect the browser conflicts .
- Detect SVG conflicts .
- Replace the current node with the correct position of a given node
- Run the UI of the diag given script tag .
- Make an inline SVG image
- function call when tree is loaded
- Make a text layer text for the given parameters .
- Watch mutations and observe
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myDrive Examples and Code Snippets
Trending Discussions on myDrive
ANSWERAnswered 2022-Apr-09 at 21:47
I thought we can simply use cv2.floodFill, and fill the white background with red color.
The issue is that the image is not clean enough - there are JPEG artifacts, and rough edges.
cv2.inRange may bring us closer, but assuming there are some white tulips (that we don't want to turn into red), we may have to use
floodFill for filling only the background.
I came up with the following stages:
- Convert from RGB to HSV color space.
- Apply threshold on the saturation channel - the white background is almost zero in HSV color space.
- Apply opening morphological operation for removing artifacts.
floodFill, on the threshold image - fill the background with the value 128.
The background is going to be 128.
Black pixels inside the area of the tulips is going to be 0.
Most of the tulips area stays white.
- Set all pixels where threshold equals 128 to red.
I upload the data in BatchDataset using the image_dataset_from_directory method...
ANSWERAnswered 2022-Mar-29 at 12:07
You can try using
tf.py_function to integrate
PIL operations in graph mode. Here is an example with a batch size of 1 to keep it simple (you can change the batch size afterwards):
I'm trying to train a custom COCO-format dataset with Detectron2 on PyTorch. My datasets are json files with the aforementioned COCO-format, with each item in the "annotations" section looking like this:
The code for setting up Detectron2 and registering the training & validation datasets are as follows:...
ANSWERAnswered 2022-Mar-29 at 11:17
It's difficult to give a concrete answer without looking at the full annotation file, but a
KeyError exception is raised when trying to access a key that is not in a dictionary. From the error message you've posted, this key seems to be
This is not in your code snippet, but before even getting into network training, have you done any exploration/inspections using the registered datasets? Doing some basic exploration or inspections would expose any problems with your dataset so you can fix them early in your development process (as opposed to letting the trainer catch them, in which case the error messages could get long and confounding).
In any case, for your specific issue, you can take the registered training dataset and check if all annotations have the
'segmentation' field. A simple code snippet to do this below.
I am new in federated learning I am currently experimenting with a model by following the official TFF documentation. But I am stuck with an issue and hope I find some explanation here.
I am using my own dataset, the data are distributed in multiple files, each file is a single client (as I am planning to structure the model). and the dependant and independent variables have been defined.
Now, my question is how can I split the data into training and testing sets in each client(file) in federated learning? like what we -normally- do in the centralized ML models The following code is what I have implemented so far: note my code is inspired by the official documentation and this post which is almost similar to my application, but it aims to split the clients as training and testing clients itself while my aim is to split the data inside these clients....
ANSWERAnswered 2022-Mar-10 at 13:35
See this tutorial. You should be able to create two datasets (train and test) based on the clients and their data:
I'm trying in google colab to get the file ID of a file stored on my google drive.
When the file is created by a function inside google colab, I get a file ID in the form of "local-xxx" instead of the actual file ID. When the file is manually uploaded to google drive, I get the correct ID.
Could you please help me fix this? Posting my code below...
ANSWERAnswered 2022-Mar-09 at 13:01
You might want to check out this answer to a related question.
It contains the implementation of a workaround based on the following observation (from another answer in the same thread):
Note: If you are using this in some type of script that is creating new files/folders and quickly reading the 'user.drive.id' afterwards, be aware that it can take many seconds for the "real" file id to be generated. If you read the value of 'user.drive.id' and it starts with 'local', this means that it has not yet generated an actual file id. In my opinion, the best way to deal with this is to create an asynchronous loop that sleeps between checks, and then returns the file id once it no longer starts with 'local'.
ps. I would rather post this as a comment, but I don't have enough rep points yet ;-)
UPDATE- Very new to python, How to clean the text from everything but Arabic letters. I used regex function but without success.
This is my code...
ANSWERAnswered 2021-Sep-30 at 00:11
As far as I understood you. You want just to clean non-arabic chars (so chars like 1 @ ? gonna not be deleted).
If you want another char to be deleted just add it to charsotdelete.
If you have any questions let me know.
I have many images in a folder and I am want to delete images of the same size. My code below, using PIL, works but I want to know if there is a more efficient way to achieve this....
ANSWERAnswered 2022-Feb-23 at 09:16
You can keep a dictionary of sizes and delete any images that have a size that have already been seen. That way you don't need a nested loop, and don't have to create Image objects for the same file multiple times.
I am facing an error while running my model.
Input to reshape is a tensor with 327680 values, but the requested shape requires a multiple of 25088
I am using (256 * 256) images. I am reading the images from drive in google colab. Can anyone tell me how to get rid of this error?
my colab code:...
ANSWERAnswered 2022-Feb-15 at 09:17
The problem is the default shape used for the
VGG19 model. You can try replacing the input shape and applying a
Flatten layer just before the output layer. Here is a working example:
I built a cnn model that classifies facial moods as happy , sad, energetic and neutral faces. I used Vgg16 pre-trained model and freezed all layers. After 50 epoch of training my model's test accuracy is 0.65 validatation loss is about 0.8 .
My train data folder has 16000(4x4000) , validation data folder has 2000(4x500) and Test data folder has 4000(4x1000) rgb images.
1)What is your suggestion to increase the model accuracy?
2)I have tried to do some prediction with my model , predicted class is always same. What can cause the problem?
What I Have Tried So Far ?
- Add dropout layer (0.5)
- Add Dense (256, relu) before last layer
- Shuff the train and validation datas.
- Decrease the learning rate to 1e-5
But I could not the increase validation and test accuracy.
ANSWERAnswered 2022-Feb-12 at 00:10
Well a few things. For training set you say you have 16,0000 images. However with a batch size of 32 and steps_per_epoch= 100 then for any given epoch you are only training on 3,200 images. Similarly you have 2000 validation images, but with a batch size of 32 and validation_steps = 5 you are only validating on 5 X 32 = 160 images. Now Vgg is an OK model but I don't use it because it is very large which increases the training time significantly and there are other models out there for transfer learning that are smaller and even more accurate. I suggest you try using EfficientNetB3. Use the code
I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:...
ANSWERAnswered 2022-Feb-07 at 09:19
It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason
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MongoDB (Unless using a service like Atlas)
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