Annotation Edit 1 9 81 Download Free

1. Annotation Edit 1 9 81 download free. full
2. Annotation Edit 1 9 81 download free. full Version

Annotation free download - FastStone Capture, XPS Annotator, PDF Annotator, and many more programs. Download Now ZeitAnker Annotation Edit gives you a sophisticated interface to attribute and subtitle video or audio in very short time. Therefore we developed techniques hardly found elsewhere and took the chance to build a clean and extensible product from scratch.

Support new data format¶

Nov 11, 2019 Download annotation-tool for free. Annotators can upload a document to the server, then the server automa. Annotators can upload a document to the server, then the server automatically split the document into sentences. The annotators can annotate the attributes, and at the same time they can leave notes for each selection.

To support a new data format, you can either convert them to existing formats (COCO format or PASCAL format) or directly convert them to the middle format. You could also choose to convert them offline (before training by a script) or online (implement a new dataset and do the conversion at training). In MMDetection, we recommand to convert the data into COCO formats and do the conversion offline, thus you only need to modify the config's data annotation pathes and classes after the conversion to your data.

Reorganize new data formats to existing format¶

The simplest way is to convert your dataset to existing dataset formats (COCO or PASCAL VOC).

The annotation json files in COCO format has the following necessary keys:

There are three necessary keys in the json file:

• images: contains a list of images with their informations like file_name, height, width, and id.
• annotations: contains the list of instance annotations.
• categories: contains the list of categories names and their ID.

After the data pre-processing, there are two steps for users to train the customized new dataset with existing format (e.g. COCO format):

1. Modify the config file for using the customized dataset.
2. Check the annotations of the customized dataset.

Here we give an example to show the above two steps, which uses a customized dataset of 5 classes with COCO format to train an existing Cascade MaskRCNN R50 FPN detector.

1. Modify the config file for using the customized dataset¶

There are two aspects involved in the modification of config file:

1. The data field. Specifically, you need to explicitly add the classes fields in data.train, data.val and data.test.
2. The num_classes field in the model part. Explicitly over-write all the num_classes from default value (e.g. 80 in COCO) to your classes number.

In configs/my_custom_config.py:

2. Check the annotations of the customized dataset¶

Assuming your customized dataset is COCO format, make sure you have the correct annotations in the customized dataset:

1. The length for categories field in annotations should exactly equal the tuple length of classes fields in your config, meaning the number of classes (e.g. 5 in this example).
2. The classes fields in your config file should have exactly the same elements and the same order with the name in categories of annotations. MMDetection automatically maps the uncontinuous id in categories to the continuous label indices, so the string order of name in categories field affects the order of label indices. Meanwhile, the string order of classes in config affects the label text during visualization of predicted bounding boxes.
3. The category_id in annotations field should be valid, i.e., all values in category_id should belong to id in categories.

Here is a valid example of annotations:

We use this way to support CityScapes dataset. The script is in cityscapes.py and we also provide the finetuning configs.

Note

1. For instance segmentation datasets, MMDetection only supports evaluating mask AP of dataset in COCO format for now.
2. It is recommanded to convert the data offline before training, thus you can still use CocoDataset and only need to modify the path of annotations and the training classes.

Reorganize new data format to middle format¶

It is also fine if you do not want to convert the annotation format to COCO or PASCAL format.Actually, we define a simple annotation format and all existing datasets areprocessed to be compatible with it, either online or offline.

The annotation of a dataset is a list of dict, each dict corresponds to an image.There are 3 field filename (relative path), width, height for testing,and an additional field ann for training. ann is also a dict containing at least 2 fields:bboxes and labels, both of which are numpy arrays. Some datasets may provideannotations like crowd/difficult/ignored bboxes, we use bboxes_ignore and labels_ignoreto cover them.

Here is an example.

There are two ways to work with custom datasets.

• online conversion

  You can write a new Dataset class inherited from CustomDataset, and overwrite two methodsload_annotations(self,ann_file) and get_ann_info(self,idx),like CocoDataset and VOCDataset.

• offline conversion

  You can convert the annotation format to the expected format above and save it toa pickle or json file, like pascal_voc.py.Then you can simply use CustomDataset.

An example of customized dataset¶

Assume the annotation is in a new format in text files.The bounding boxes annotations are stored in text file annotation.txt as the following

We can create a new dataset in mmdet/datasets/my_dataset.py to load the data.

Then in the config, to use MyDataset you can modify the config as the following

Customize datasets by dataset wrappers¶

MMDetection also supports many dataset wrappers to mix the dataset or modify the dataset distribution for training.Currently it supports to three dataset wrappers as below:

• RepeatDataset: simply repeat the whole dataset.
• ClassBalancedDataset: repeat dataset in a class balanced manner.
• ConcatDataset: concat datasets.

Repeat dataset¶

We use RepeatDataset as wrapper to repeat the dataset. For example, suppose the original dataset is Dataset_A, to repeat it, the config looks like the following

Class balanced dataset¶

We use ClassBalancedDataset as wrapper to repeat the dataset based on categoryfrequency. The dataset to repeat needs to instantiate function self.get_cat_ids(idx)to support ClassBalancedDataset.For example, to repeat Dataset_A with oversample_thr=1e-3, the config looks like the following

You may refer to source code for details.

Concatenate dataset¶

There are three ways to concatenate the dataset.

1. If the datasets you want to concatenate are in the same type with different annotation files, you can concatenate the dataset configs like the following.

   If the concatenated dataset is used for test or evaluation, this manner supports to evaluate each dataset separately. To test the concatenated datasets as a whole, you can set separate_eval=False as below.

2. In case the dataset you want to concatenate is different, you can concatenate the dataset configs like the following.

   If the concatenated dataset is used for test or evaluation, this manner also supports to evaluate each dataset separately.

3. We also support to define ConcatDataset explicitly as the following.

   This manner allows users to evaluate all the datasets as a single one by setting separate_eval=False.

Note:

1. The option separate_eval=False assumes the datasets use self.data_infos during evaluation. Therefore, COCO datasets do not support this behavior since COCO datasets do not fully rely on self.data_infos for evaluation. Combining different types of datasets and evaluating them as a whole is not tested thus is not suggested.
2. Evaluating ClassBalancedDataset and RepeatDataset is not supported thus evaluating concatenated datasets of these types is also not supported.

A more complex example that repeats Dataset_A and Dataset_B by N and M times, respectively, and then concatenates the repeated datasets is as the following.

Modify Dataset Classes¶

With existing dataset types, we can modify the class names of them to train subset of the annotations.For example, if you want to train only three classes of the current dataset,you can modify the classes of dataset.The dataset will filter out the ground truth boxes of other classes automatically.

MMDetection V2.0 also supports to read the classes from a file, which is common in real applications.For example, assume the classes.txt contains the name of classes as the following.

Users can set the classes as a file path, the dataset will load it and convert it to a list automatically.

Note:

• Before MMDetection v2.5.0, the dataset will filter out the empty GT images automatically if the classes are set and there is no way to disable that through config. This is an undesirable behavior and introduces confusion because if the classes are not set, the dataset only filter the empty GT images when filter_empty_gt=True and test_mode=False. After MMDetection v2.5.0, we decouple the image filtering process and the classes modification, i.e., the dataset will only filter empty GT images when filter_empty_gt=True and test_mode=False, no matter whether the classes are set. Thus, setting the classes only influences the annotations of classes used for training and users could decide whether to filter empty GT images by themselves.
• Since the middle format only has box labels and does not contain the class names, when using CustomDataset, users cannot filter out the empty GT images through configs but only do this offline.
• Please remember to modify the num_classes in the head when specifying classes in dataset. We implemented NumClassCheckHook to check whether the numbers are consistent since v2.9.0(after PR#4508).
• The features for setting dataset classes and dataset filtering will be refactored to be more user-friendly in the future (depends on the progress).

You're viewing help content for version:

• 6.3

Tagalicious v1 5 5 – clean up your music collection. Adding content to the pages of your website is often subject to discussions prior to it actually being published. To aid this, many components directly related to content (as opposed, for example, to layout) allow you to add an annotation.

An annotation places a colored marker/sticky-note on the page. The annotation allows you (or other users) to leave comments and/or questions for other authors/reviewers.

Note:

Here we give an example to show the above two steps, which uses a customized dataset of 5 classes with COCO format to train an existing Cascade MaskRCNN R50 FPN detector.

1. Modify the config file for using the customized dataset¶

There are two aspects involved in the modification of config file:

1. The data field. Specifically, you need to explicitly add the classes fields in data.train, data.val and data.test.
2. The num_classes field in the model part. Explicitly over-write all the num_classes from default value (e.g. 80 in COCO) to your classes number.

In configs/my_custom_config.py:

2. Check the annotations of the customized dataset¶

Assuming your customized dataset is COCO format, make sure you have the correct annotations in the customized dataset:

1. The length for categories field in annotations should exactly equal the tuple length of classes fields in your config, meaning the number of classes (e.g. 5 in this example).
2. The classes fields in your config file should have exactly the same elements and the same order with the name in categories of annotations. MMDetection automatically maps the uncontinuous id in categories to the continuous label indices, so the string order of name in categories field affects the order of label indices. Meanwhile, the string order of classes in config affects the label text during visualization of predicted bounding boxes.
3. The category_id in annotations field should be valid, i.e., all values in category_id should belong to id in categories.

Here is a valid example of annotations:

We use this way to support CityScapes dataset. The script is in cityscapes.py and we also provide the finetuning configs.

Note

1. For instance segmentation datasets, MMDetection only supports evaluating mask AP of dataset in COCO format for now.
2. It is recommanded to convert the data offline before training, thus you can still use CocoDataset and only need to modify the path of annotations and the training classes.

Reorganize new data format to middle format¶

It is also fine if you do not want to convert the annotation format to COCO or PASCAL format.Actually, we define a simple annotation format and all existing datasets areprocessed to be compatible with it, either online or offline.

The annotation of a dataset is a list of dict, each dict corresponds to an image.There are 3 field filename (relative path), width, height for testing,and an additional field ann for training. ann is also a dict containing at least 2 fields:bboxes and labels, both of which are numpy arrays. Some datasets may provideannotations like crowd/difficult/ignored bboxes, we use bboxes_ignore and labels_ignoreto cover them.

Here is an example.

There are two ways to work with custom datasets.

• online conversion

  You can write a new Dataset class inherited from CustomDataset, and overwrite two methodsload_annotations(self,ann_file) and get_ann_info(self,idx),like CocoDataset and VOCDataset.

• offline conversion

  You can convert the annotation format to the expected format above and save it toa pickle or json file, like pascal_voc.py.Then you can simply use CustomDataset.

An example of customized dataset¶

Assume the annotation is in a new format in text files.The bounding boxes annotations are stored in text file annotation.txt as the following

We can create a new dataset in mmdet/datasets/my_dataset.py to load the data.

Then in the config, to use MyDataset you can modify the config as the following

Customize datasets by dataset wrappers¶

MMDetection also supports many dataset wrappers to mix the dataset or modify the dataset distribution for training.Currently it supports to three dataset wrappers as below:

• RepeatDataset: simply repeat the whole dataset.
• ClassBalancedDataset: repeat dataset in a class balanced manner.
• ConcatDataset: concat datasets.

Repeat dataset¶

We use RepeatDataset as wrapper to repeat the dataset. For example, suppose the original dataset is Dataset_A, to repeat it, the config looks like the following

Class balanced dataset¶

We use ClassBalancedDataset as wrapper to repeat the dataset based on categoryfrequency. The dataset to repeat needs to instantiate function self.get_cat_ids(idx)to support ClassBalancedDataset.For example, to repeat Dataset_A with oversample_thr=1e-3, the config looks like the following

You may refer to source code for details.

Concatenate dataset¶

There are three ways to concatenate the dataset.

1. If the datasets you want to concatenate are in the same type with different annotation files, you can concatenate the dataset configs like the following.

   If the concatenated dataset is used for test or evaluation, this manner supports to evaluate each dataset separately. To test the concatenated datasets as a whole, you can set separate_eval=False as below.

2. In case the dataset you want to concatenate is different, you can concatenate the dataset configs like the following.

   If the concatenated dataset is used for test or evaluation, this manner also supports to evaluate each dataset separately.

3. We also support to define ConcatDataset explicitly as the following.

   This manner allows users to evaluate all the datasets as a single one by setting separate_eval=False.

Note:

1. The option separate_eval=False assumes the datasets use self.data_infos during evaluation. Therefore, COCO datasets do not support this behavior since COCO datasets do not fully rely on self.data_infos for evaluation. Combining different types of datasets and evaluating them as a whole is not tested thus is not suggested.
2. Evaluating ClassBalancedDataset and RepeatDataset is not supported thus evaluating concatenated datasets of these types is also not supported.

A more complex example that repeats Dataset_A and Dataset_B by N and M times, respectively, and then concatenates the repeated datasets is as the following.

Modify Dataset Classes¶

With existing dataset types, we can modify the class names of them to train subset of the annotations.For example, if you want to train only three classes of the current dataset,you can modify the classes of dataset.The dataset will filter out the ground truth boxes of other classes automatically.

MMDetection V2.0 also supports to read the classes from a file, which is common in real applications.For example, assume the classes.txt contains the name of classes as the following.

Users can set the classes as a file path, the dataset will load it and convert it to a list automatically.

Note:

• Before MMDetection v2.5.0, the dataset will filter out the empty GT images automatically if the classes are set and there is no way to disable that through config. This is an undesirable behavior and introduces confusion because if the classes are not set, the dataset only filter the empty GT images when filter_empty_gt=True and test_mode=False. After MMDetection v2.5.0, we decouple the image filtering process and the classes modification, i.e., the dataset will only filter empty GT images when filter_empty_gt=True and test_mode=False, no matter whether the classes are set. Thus, setting the classes only influences the annotations of classes used for training and users could decide whether to filter empty GT images by themselves.
• Since the middle format only has box labels and does not contain the class names, when using CustomDataset, users cannot filter out the empty GT images through configs but only do this offline.
• Please remember to modify the num_classes in the head when specifying classes in dataset. We implemented NumClassCheckHook to check whether the numbers are consistent since v2.9.0(after PR#4508).
• The features for setting dataset classes and dataset filtering will be refactored to be more user-friendly in the future (depends on the progress).

You're viewing help content for version:

• 6.3

Tagalicious v1 5 5 – clean up your music collection. Adding content to the pages of your website is often subject to discussions prior to it actually being published. To aid this, many components directly related to content (as opposed, for example, to layout) allow you to add an annotation.

An annotation places a colored marker/sticky-note on the page. The annotation allows you (or other users) to leave comments and/or questions for other authors/reviewers.

Note:

Annotation Edit 1 9 81 download free. full

The definition of an individual component type determines whether adding an annotation is possible (or not) on instances of that component.

Note:

Annotations created in the Classic UI will be shown in the touch-enabled UI. However sketches are UI-specific and they are only shown in the UI in which they were created.

Caution:

Annotation Edit 1 9 81 download free. full Version

Deleting a resource (e.g. paragraph) deletes all the annotations and sketches attached to that resource irrespective of their position on the page as a whole.

Note:

Depending on your requirements you can also develop a workflow to send notifications when annotations are added, updated, or deleted.

Day one 2 1 2 – maintain a daily journal. A special mode is used for creating and viewing annotations.

Note:

Don't forget that comments are also available for providing feedback on a page.

Note:

You can annotate a variety of resources:

The Annotate mode allows you to create, edit, move or delete annotations on your content:

You can enter Annotate mode using the icon in the toolbar (top right) when editing a page:

Note:

To exit Annotation mode tap/click the Annotate icon (x symbol) at the right of the top toolbar.

Click/tap the Add Annotation icon (plus symbol at the left of the toolbar) to start adding annotations.


Note:

To stop adding annotations (and return to viewing) tap/click the Cancel icon (x symbol in a white circle) at the left of the top toolbar.

Click/tap the required component (components that can be annotated will be highlighted with a blue border) to add the annotation and open the dialog:

Here you can use the appropriate field and/or icon to:

• Enter the annotation text.
• Create a sketch (lines and shapes) to highlight an area of the component.
  The cursor will change to a crosswire when you are creating a sketch. You can draw multiple distinct lines. The sketch line reflects the annotation color and can be either an arrow, circle, or oval.

You can close the annotation dialog by clicking/tapping outside the dialog. A truncated view (the first word) of the annotation, together with any sketches, is shown:

After you have finished editing a specific annotation, you can:

• Click/tap the text marker to open the annotation. Once open you can view the full text, make changes or delete the annotation.
  • Sketches cannot be deleted independently of the annotation.
• Reposition the text marker.
• Click/tap on a sketch line to select that sketch and drag it to the desired position.
  
• Move, or copy, a component
  • Any related annotations and their sketches will also be moved or copied and their position in relation to the paragraph will remain the same.

To exit Annotation mode, and return to the mode previously used, tap/click the Annotate icon (x symbol) at the right of the top toolbar.

Note:

Annotations can not be added to a page that has been locked by another user.

Annotations do not appear in Edit mode, but the badge at the top right of the toolbar shows how many annotations exist for the current page. The badge replaces the default Annotations icon, but still functions as a quick link that toggles to/from the Annotate mode: