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98 results for “CNN”
SCM-CNN: A Robust Deep Learning Matting Model for Cloud Removal in Optical Imagery
<p>This is a dataset that can be used for cloud detection and cloud opacity estimation. The data is saved in python-numpy form, and stored in dictionary :dict_keys(['OriginImage', 'Gimage', 'Alpha', 'Trimap', 'CloudMaxDN']) represents the cloud-free remote sensing image, cloud remote sensing image, cloud opacity, trilateration information and cloud brightness respectively. The command {np.load("Path",allow_pickle=True).item()} is used to read, where "Path" is the corresponding path to the file.</p>
Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats
<p>Here we introduce the dataset utilized in our published paper entitled "<a href="https://www.sciencedirect.com/science/article/pii/S2666154324002114">Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats</a>".</p> <p>Contained within the "bcs" folder are all the videos collected for this study. Each video file is named with a format denoting its respective details. The first number signifies the sequence of collection, the second denotes the ear tag, and the final figure represents the body condition score (BCS) value.</p> <p>For example: "1_158734_2.50" indicates the first sampling of an animal with the ear tag "158734" and a BCS value of "2.50".</p> <p>Additionally, we provide two Python scripts in this repository. The first script, "Video2Frame.py", facilitates the splitting of videos into individual frames. The second script, "Frames2npy.py", converts these frames into two numpy-friendly files with the extension ".npy". These files contain both the images ("X_train_bcs300.npy") and their corresponding labels ("Y_train_bcs300.npy").</p> <p>Furthermore, for the convenience of swift experimentation, we have included the desired .npy files within the repository.</p> <p>To load these files into your Python environment, you can use the following code snippet:</p> <div> <div>th4figs = '/content/drive/MyDrive/compag_2023/'</div> <br> <div>path4images = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/X_train_bcs300.npy"</div> <div>Xtrain = np.load(path4images)</div> <br> <div>path4labels = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/Y_train_bcs300.npy"</div> <div>Ytrain = np.load(path4labels).astype(float)</div> <br> <div>print("X train : ", Xtrain.shape)</div> <div>print("Y train : ", Ytrain.shape)</div> <div> <div> <div> <div> <div> <div> <div> </div> </div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> <pre>X train : (5332, 300, 300, 3) Y train : (5332,)<br> </pre> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Figure 1. CNN architecture (adopted from Krizhevsky et al. '12)-Measuring Customer Behavior with Deep Convolutional Neural Networks
<p>The architecture of a CNN can be described as following. A small pixel region goes to input neurons and then connects to a first convolution hidden layer (Figure1). There we can see a set of learnable filters, which are activated during the presentation some particular type of feature in pixel region in the input. On this phase, CNN does shift invariance, which is carried by feature map. Subsampling layer goes next. There we have two processes: local averaging and sampling. As a result, we get declining resolution of feature map. To correspond this task CNN needs supervised learning. Before starting the experiment, we gave a set of labeled videos with different emotional experience. The system analyses images and finds similar features. Then the system creates a map, where it arranges videos in accordance with similar features. Thereby, images with similar emotions form certain class. To test the system, we add other videos and correct the system when it refers them improperly. The proposed model consists of four convolutional layers, followed by max-pooling layers, and three fully-connected layers with a final classificatory presented with MLP (with six basic outputs, corresponding to basic emotions for emotion classification and two outputs for motion classification for typical and non-typical behavior). The input data was presented as infrared camera output.</p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 5. Results from CNN and ANNSVM that used 1Dimg and 2Dimg
<p>We compared the results of CNN_1Dimg, CNN_2Dimg, ANNSVM_1Dimg, and ANNSVM_2Dimg to confirm the validity of ANNSVM when applied to images. The 1Dimg represented the dataset of one-dimensional images, while 2Dimg represented the dataset of twodimensional images. Results are shown in Figure 5. </p>
Mask R-CNN on NYUv2
<p><strong>Mask R-CNN on NYUv2</strong></p> <p>This repository mainly contains information from the execution of the <a href="https://github.com/facebookresearch/maskrcnn-benchmark">Mask R-CNN network </a>[1] on images from the <a href="https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html">NYUv2 dataset</a> [2] as well as additional metadata. It was created for analyzing the output of Mask R-CNN and post-processing it using contextual information for improving its performance. This work has been carried out by <a href="http://mapir.isa.uma.es/jotaraul">Dr. Jose-Raul Ruiz-Sarmiento</a> (MAPIR group, University of Málaga) and <a href="https://lishuda.wordpress.com/">Dr. Shuda Li</a> (AVG group, University of Oxford) in the scope of the European project<a href="http://www.movecare-project.eu/"> MoveCare: Multiple-actOrs Virtual Empathic CARgiver for the Elder</a> (Ref: 732158).</p> <p>Concretely, <strong>this repository includes</strong>:</p> <p>- metadata:<br> + coco_nyu_mapping.txt: Mapping between the categories in COCO dataset and those in NYUv2.<br> + coco_object_categories.txt: Object categories considered in COCO dataset.<br> + nyu_object_categories.txt: Object categories used in NYUv2 dataset.<br> + nyu_scene_categories.txt: Scene categories considered in NYUv2.<br> + objects_and_categories_in_images.txt: For each image in NYUv2, the categories of the appearing objects.</p> <p>- nyu_content:<br> + masks_in_X (Where X is the image index)<br> - Y.png: Where Y is the object index in the image, represents the binary mask of that object.<br> - pixels_labelled.png: Binary mask indicating the labelled pixels in image X.<br> + bboxesX.txt: Where X is the image index, includes the ground truth bounding boxes of the objects in it. Format is: min_x min_y max_x max_y.</p> <p>- preds:<br> + X: Where X is the image index.<br> - Y.png: Where Y is the object index in the image, as detected by Mask R-CNN. Binary image containing the mask of such detected object.<br> + X.txt: Where X is the image index. File containing the objects detected by Mask R-CNN, including: idx class score min_x min_y max_x max_y masks_file, being min_x min_y max_x and max_y bounding box information, while masks_file refers to X/Y.png as described above.<br> + result_X.png: Where X is the image index. Image showing the detections with a socre higher than 0.3.<br> + gt_iou_X: Where X is the image index.<br> - Y: Where Y is the index of the detected object.<br> + Z.png Where Z is the index of the object in the ground truth. Image showing the masks of both objects, Y and Z, for visually checking their overlapping.<br> - Y.txt: Where Y is the index of the detected object. File containing:<br> + The intersection ratio of the object mask Y with the labelled part of the image.<br> + The IoU value for the mask of object Y and those of ground truth objects.<br> <br> <br> <strong>References:</strong></p> <p>[1] He, Kaiming, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. "Mask r-cnn." In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969. 2017.<br> [2] Silberman, Nathan, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. "Indoor segmentation and support inference from rgbd images." In European Conference on Computer Vision, pp. 746-760. Springer, Berlin, Heidelberg, 2012.</p>
starchID: CNN based identification of microscopic starch images
<p>This is a collection of 500px images of 10 different starch types.</p> <p>It comprises about 40000 images, i.e. 4000 per species.</p> <p>Imaging was performed with equipment maintained by the Center for Microscopy and Image Analysis, University of Zurich.</p> <p>Photos were shot using a Leica Dmi8 light microscopy system (20x) coupled to a Leica DMC6200 digital camera.</p> <p>The data are part of a course assignment for the CAS Artificial Intelligence at the Lucerne University of Applied Sciences and Arts.</p> <p>For raw data and other inquiries just text <a href="mailto:info@peterstaub.is">me</a>.</p> <p>Jupyter notebooks may be found <a href="https://github.com/peterausderschweiz/starchID/">here</a>.</p>
CNN models and training, validation and test datasets for "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"
<p>Convolutional neural network (CNN) models and their respective training, validation and test datasets used in manuscript:</p> <p>Tuomo Hartonen, Teemu Kivioja and Jussi Taipale, "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"</p>
CNN weight data for "Model identification of neural encoding (MINE)" publication - Set 2
<p>This dataset contains the weights of fit CNN models generated during the analysis of the zebrafish thermoregulation dataset and the Musall et al. mouse dataset processed by MINE. This set contains the last fish and the mouse MINE model weights. The other 24 fish are contained in Set 1.</p>
CNN weight data for "Model identification of neural encoding (MINE)" publication - Set 1
<p>This dataset contains the weights of fit CNN models generated during the analysis of the zebrafish thermoregulation dataset processed by MINE. This set contains 24/25 fish. The last fish and mouse MINE model weights are contained in Set 2.</p>
CNNpred: CNN-based stock market prediction using a diverse set of variables
<p>This dataset contains several daily features of S&P 500, NASDAQ Composite, Dow Jones Industrial Average, RUSSELL 2000, and NYSE Composite from 2010 to 2017. It covers features from various categories of technical indicators, futures contracts, price of commodities, important indices of markets around the world, price of major companies in the U.S. market, and treasury bill rates. Sources and thorough description of features have been mentioned in the paper of "CNNpred: CNN-based stock market prediction using a diverse set of variables".</p>
Distorted sound for evaluation of CNN
<p>Distorted sound for evaluation of CNN, original files are from urbansound8k. </p>
Leaf Vein Network CNN Images
<p>This download site contains the CNN vein network predictions and set of Matlab programs that were used for the analyses in Xu <em>et al.</em>, (2020) and Blonder <em>et al.,</em> (2020). These require Matlab 2020a or later. They may work on earlier versions of MatLab, but this has not been tested and cannot be guaranteed.</p> <p>The files are as follows:</p> <ol> <li>Zip files (e.g. BEL_downsampled_images.zip) containing a complete set of images of leaf vein predictions from a fully trained convolutional neural network (CNN), along with the ground truth data. Each folder in the unzipped file contains a sample represented by a CODE with format X-TY-BZ. X represents the name of a plot in the Global Ecosystems Monitoring network database (e.g. 'BEL'). Tree (T) Y indicates the number of a tree within a plot (e.g. '101') and Z represents the light stratum of the canopy where the leaf was collected (either 'S' for 'sunlit' or 'SH' for 'shaded').</li> <li>A set of Matlab programs (Matlab files.zip) to compare the CNN predictions against other vein extraction approaches.</li> <li>A Matlab Readme file with instructions on how to run the analyses.</li> </ol> <p><strong>References</strong></p> <p><strong>Software GUI:</strong></p> <p>Xu, H., Blonder, B., Jodra, M., Malhi, Y. and Fricker, M.D. (2020) Automated and accurate segmentation of leaf venation networks via deep learning. <strong>New Phytol</strong>. (In press).</p> <p><strong>Analysis of trait data:</strong></p> <p>Blonder, B., S. Both, M. Jodra, H. Xu, M. Fricker, I. S. Matos, N. Majalap, D. F. R. P. Burslem, Y. Teh and Y. Malhi (2020) Linking functional traits to multiscale statistics of leaf venation networks. <strong>New Phytol</strong>. (In press).</p> <p><strong>Original image data set and ground truths</strong></p> <p>Blonder, B., Both, S., Jodra, M., Majalap, N., Burslem, D., Teh, Y. A., and Malhi, Y. (2019) Leaf venation networks of Bornean trees: images and hand‐traced segmentations. <strong>Ecology </strong>100: e02844.<a href="https://doi.org/10.1002/ecy.2844">10.1002/ecy.2844</a>.</p> <p>Available from: <a href="https://ora.ox.ac.uk/objects/uuid:de65fc07-4b8f-4277-a6c4-82836afbdeb3">https://ora.ox.ac.uk/objects/uuid:de65fc07-4b8f-4277-a6c4-82836afbdeb3</a></p>
XSum and CNN with paraphrases of summaries
<p>These datasets contain the original datasets and paraphrases of the summaries.</p><p>CNN/DailyMail is available at https://huggingface.co/datasets/cnn_dailymail released under the Apache 2.0 license. </p><p>XSum is available at https://huggingface.co/datasets/EdinburghNLP/xsum and was originally released under the MIT license.</p><p>The paraphraser is https://huggingface.co/humarin/chatgpt_paraphraser_on_T5_base available under the OpenRAIL license.</p>
CNN HP loss landscapes with HPs
<p>Low-dimensional visualizations of HP loss landscapes for CNN on MINIST dataset.<br>The first subplot shows the distribution of test accuracy across the landscape.<br>The remaining plots depict the distribution of HP values across the landscape.<br>The colors indicate ranks of the values. </p>
CNN-Based Forecasting of Pitch Angle-Resolved Energetic Electron Flux at MEO Using Solar Wind and Geomagnetic Data
<div> <p> CNN-Based Forecasting of Pitch Angle-Resolved Energetic Electron Flux at MEO Using Solar Wind and Geomagnetic Data. The model and test set data are provided here. Data used for training, validating, and testing the 1.8MeV channel model is also offered as an example.</p> </div> <p><strong>initial_data: </strong>The test set data has been normalized and can be used as model input.</p> <p><strong>norm para: </strong>The normalization parameters used for data processing</p> <p><strong>model_test_dataset_performance.py: </strong>The script to obtain the outputs of the models at different energy levels on the test set. Before running it, unzip “initial_data.rar” and "norm_para.rar"</p> <p><strong>full_dataset_for_rept_ch0: </strong>Data used for training, validating, and testing the 1.8MeV channel model. It's not essential for model_test_dataset_performance.py</p>
CNN-Driven DBMS Estimation from Aeromagnetic Data and Applications in Southern Peninsular India
<p>Datasets for the manuscript titled </p> <pre><span>CNN-trained </span><span>centroid</span><span> method for estimating Depth to the Bottom of the Magnetic Sources (DBMS) and its application to the south Indian shield</span></pre>
Application of optimized CNN to fixture layout in automotive parts. Training Dataset.
<p>Fixture layout has aroused substantial interest in the research community during the last decades. It affects the design and production of fixtures themselves, and therefore manufacturing costs. While fixturing may seem simple in conception, it requires expertise and well-trained engineers. </p> <p>A general principle, usually called the 3-2-1 locating principle, ensures fixing an object, restringing its degrees of freedom. Fixtures must always comply with the principle. While most research approaches automation of the fixture layout using optimization or rule-based frameworks, this paper proposes supervised learning. The presented framework solves the 3-2-1 locating principle for sheet metal designs based on the experience of previous designs, using automotive b-pillars as a test study.</p> <p>There are three main contributions. 1. A novel idea to introduce sheet metal design data in a Convolutional Neural Network (CNN), projecting the geometry over a plane. The Z coordinate transforms into gray-scale pixel values, generating a topographic map. 2. The framework reuses knowledge about fixturing to layout new workpieces. The framework is an add-in integrated with the CAD environment. 3. A hyperparameter-tuned CNN for regression generates the final output.</p> <p>The results show high accuracy (≈ 100%) in classifying b-pillars and fast convergence in regression, proving model usability for industrial cases.</p> <p> </p> <p>Source Code: https://colab.research.google.com/drive/18lS83mZEdwY5S-41pvcGXgUP6GbKpun_?usp=sharing</p>
CNN-based Network Application for Petrophysical Parameter Inversion: Sensitivity Analysis of Input-output Parameters and Network Architecture
<p>The uploaded file includes four groups of data. They have original elastic and reservoir parameters data, the k=5 and k=25 (k means the sampling interval in inline and crossline.), and testing dataset.</p>
Prediction Result of Next Monochrome Ionogram frame using LSTM-CNN Model
<p>Video of next frame ionogram prediction result using simple LSTM-CNN model</p> <p>Paper published in The 9Th International Seminar on Aerospace Science and Technology ISAST 2022</p>
Interpretable Deep Learning for Probabilistic MJO Prediction: CNN Forecasts
<p>This repository contains data produced for the paper "Interpretable Deep Learning for Probabilistic MJO Prediction" by A. Delaunay and H. M. Christensen (2021).</p> <p>>> mu_ens_XX.pt<br> contains the mean forecasts from each ensemble member at a lead time of XX days</p> <p>>> cov_alea_XX.pt<br> contains the aleatoric predictions of each ensemble member at a lead time of XX days</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.