Disk labelers

Pill with imprint M 97 is Orange, Three-sided and has been identified as Fluphenazine Hydrochloride 10 mg. It is supplied by Mylan Pharmaceuticals Inc.Fluphenazine is used in the treatment of Psychosis and belongs to the drug class phenothiazine antipsychotics. FDA has not classified the drug for risk during pregnancy. Fluphenazine 10 mg is not a controlled substance under the Controlled Substances Act (CSA). Images for M 97 Fluphenazine Hydrochloride Imprint M 97 Strength 10 mg Color Orange Size 10.00 mm Shape Three-sided Availability Prescription only Drug Class Phenothiazine antipsychotics Pregnancy Category N - Not classified CSA Schedule Not a controlled drug Labeler / Supplier Mylan Pharmaceuticals Inc. Inactive Ingredients hypromelloses, magnesium stearate, microcrystalline cellulose, polyethylene glycol, corn starch, sodium lauryl sulfate, titanium dioxide, soya lecithin, polydextrose, sodium alginate, triacetin, FD&C Yellow No. 6 Note: Inactive ingredients may vary. Labelers / Repackagers NDC Code Labeler / Repackager 00378-6097 (Discontinued) Mylan Pharmaceuticals Inc. 51079-0488 (Discontinued) UDL Laboratories Inc. Get help with Imprint Code FAQs. Related images for "M 97"Further informationAlways consult your healthcare provider to ensure the information displayed on this page applies to your personal circumstances.Medical Disclaimer

Polygon and nothing else. They support some hotkey shortcuts and the application is very lightweight in general.3. Supervise.lySupervisely is an awesome web-based platform that offers an advanced annotation interface but also covers the entire process of computer vision training, including a deep learning models library that can be directly trained, tested, and improved within the platform.Price: Free community edition and enterprise pricing for the self-hosted versionFunctionalities: A great array of tools, including dots, lines, boxes, polygons, and a bitmap brush for semantic segmentation (we haven’t found their smart tool too useful though). Also includes the possibility to draw holes in polygons, which has been incredibly valuable. Another very useful feature is the option to add image and object tags and to order figures in layers. Output is in JSON files for each image or PNG masks and the platform also allows you to upload formats such as Cityscapes and COCO. In addition, there is an option to do data transformation directly on the platform.Project management: The platform offers tons of options for project management on various levels (teams, workspaces, datasets) and annotator management (labeling jobs, permissions, statistics). They also have a Data Transformation Language and a Python Notebooks option for managing the data which come in handy a lot. A couple of things missing are time statistics, as well as quality control mechanisms. Their tech support team is always available in the case of problems. The interface allows for very precise work and supports customizable hotkey shortcuts but performance has recently been slow at times, which can be pretty frustrating if the platform takes a lot of time to switch between images and record annotations.4. LabelboxLabelbox is another great web-based platform that launched in early 2018 and ever since then has been constantly updating and improving its functionalities. It also offers the possibility to integrate a human-in-the-loop by importing model predictions and seeing the consensus between the labelers and the model.Pricing: Free community edition limited to 5000 images and an enterprise versionFunctionalities: Offers a complete array of tools for annotation, such as points, lines, boxes and polygons, and has recently

Added an awesome new feature for their semantic segmentation brush — a superpixel coloring option that makes life so much easier when boundaries are clear (much like this and this open source tools). Output is as one JSON or CSV file containing all annotations or as PNG masks (however, there is one mask for every class and the user needs to figure out what to do with overlapping regions afterwards)Project management: Setting up a project is extremely easy, and there are many options for monitoring performance, including statistics on seconds needed to label an image. You can implement several quality control mechanisms, including activating automatic consensus between different labelers or setting gold standard benchmarks. You have the option to invite users (though permissions are not as granular) and to review the work of each one. The labeling interface is super user-friendly and supports hotkey shortcuts (although not customizable). One thing missing in the free version is the option to upload annotations so as to visualize or edit them.Need something else? Here are some other platforms that you can consider:Diffgram — a really promising platform still in beta that optimizes image annotation by training RCNN, will be featured in the second article in this series!RectLabel — an awesome tool for bounding boxes and polygons for MacOSProdigy — they offer a self-hosted back-end with different annotation interfaces, including image annotation with bounding boxes; pricing for their product starts at $390 for personal use (lifetime per user)DataTurks — a platform that offers many annotation capabilities; data annotated in the free version is publicly available, and enterprise pricing starts at $300 per month for small teamsImageTagger — an open source platform for collaborative image labelingFast Annotation Tool — another open source tool, using OpenCV for bounding boxes in the RotatedRect formatLabelMe — an industry classic, open source tool by MIT for polygonal annotation; precision is extremely low thoughPolygonRNN+ — available only as a demo, but still very promising; a tool that is trained on the Cityscapes dataset do generate automatic labels for self-driving cars with reinforcement learning—Dealing with large datasets and need to scale