The app knows how Directing works and automatically does most of the work for you. Shot Designer is based on the realization that neither Camera Diagrams, Shot Lists, or Storyboards by themselves give you a satisfying understanding of camera-blocking - you have to use them *together*. Shot Designer was developed by Per Holmes, the creator of the Hollywood Camera Work training. SYNC & TEAM SHARING VIA DROPBOX - Sync your scenes across all your devices.
#Shot designer pro#
MAC/PC DESKTOP VERSION - An identical Mac/PC Desktop Version is included with the Pro Version (in-app upgrade).DIRECTOR'S VIEWFINDER / STORYBOARDS - Bring in lens-accurate camera angles via the integrated Director's Viewfinder or Storyboard Import.Edit shots intuitively in the diagram, not in a confusing spreadsheet. SHOT LIST - The integrated Shot List is tied into the diagram and writes itself while you work.Previsualize the rhythm of a scene by seeing it play out. ANIMATION - Animate your characters and cameras to move around your diagram in real-time.CAMERA DIAGRAM - Shot Designer dramatically speeds up the Director's process of making camera diagrams.Impact of base dataset design on few-shot image classification. SBAI, Othman, COUPRIE, Camille, et AUBRY, Mathieu.
![shot designer shot designer](https://i1.wp.com/jonhillenbrandphotography.com/development/wp-content/uploads/2014/08/Scene_04a.jpg)
#Shot designer license#
You may find out more about the license here.
![shot designer shot designer](https://ptis.ac.th/wp-content/uploads/2021/02/Screen-Shot-2021-02-16-at-11.01.10-AM-copy.jpg)
There is an important trade-off between the number of classes and the number of images for a fixed dataset budget, which determines the optimal performance achieved by random classes sampled from a large dataset.įew-shot performance can be improved by relabeling images in each class (splitting classes) or by grouping different classes into meta-classes, depending on the initial trade-off between the number of classes and images per class. Effect of the number of classes for a fixed number of annotations ) or the training algorithm (Prototypical networks, matching Networks or cosine classifier)ī. The similarity between training classes and test classes influences the few-shot performance, regardless of the features (Oracle, MoCo or Wordnet) considered, the backbones chosen (WideResNet, ResNet18. Importance of base data and its similarity to test data In train_submitit.ipynb, we provide commands for launching multiple experiments of each table and figure in the paper. Opt.arch = 'resnet18' # architecture options: 'wrn','resnet18','conv4'. Opt.dataset = 'miniIN' # training dataset options are 'miniIN','cub' or can be passed to Trainer init as argument
![shot designer shot designer](https://en.freedownloadmanager.org/screenshots/106115.jpg)
Opt.benchmark = 'miniIN' # eval benchmark options are: 'miniIN','cub' Opt.train_type = 'CC' # cosine classifier To do that, we sample datasets from a large meta-dataset of 6000 classes (miniIN6k), then we compare the obtained performance on different benchmarks (miniIN, CUB. Especially, considering the gain from using more data and the influence of farthest base classes to test ones. Our main contribution is to demonstrate the importance of dataset design in few-shot performance and to give insights about improving it by going beyond the natural dataset labeling and better exploiting class richness in favor of creating more difficult classification tasks.įurthermore, our paper gives insights on important aspects of few-shot evaluation. We rely on a simple but effective baseline: Nearest-Neighbors (NN) with L2-normalized features, usually named Cosine Classifier. Our paper presents a systematic empirical study about the impact of the base dataset on the few-shot classification performance using given features.
#Shot designer code#
(ECCV 2020) This code is aimed at reproducing the results and figures in the ECCV 2020 paper: Impact of base dataset design on few-shot image classification - project webpage.