31 skills found · Page 1 of 2
icflorescu / Mantine DatatableThe table component for your Mantine data-rich applications, supporting asynchronous data loading, column sorting, custom cell data rendering, context menus, nesting, Gmail-style batch row selection, dark theme, and more.
BazedFrog / SongGeneration StudioClean, polished interface for Tencent’s SongGeneration. Create songs from text prompts or reference audio, with batch processing and smart model selection. Minimum Requirement: 10GB of VRAM
Danushka-Madushan / Animepahe CliA C++ command-line interface for downloading anime episodes from animepahe.si with quality selection, batch downloads, episode range selection, ZIP archive creation, export functionality and self-updating support.
gfyddha / UDSOfficial implementation of our paper: "Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning."
VamshiTeja / SMDL(IJCAI 2019) Submodular Batch Selection for Training Deep Neural Networks
LuviKunG / MipMapBiasEditorMip Map Bias Editor is an Unity Editor plugins that can perform Mip Map Bias quality selection and batching all textures in the selected folder of project.
yuji-roh / FairbatchFairBatch: Batch Selection for Model Fairness (ICLR 2021)
RAZZULLIX / Fast Topk BatchedHigh-performance batched Top-K selection for CPU inference. Up to 80x faster than PyTorch, optimized for LLM sampling with AVX2 SIMD.
wittman / S3uploadS3Upload plugin (for Piwigo web app) that uploads gallery photos to your configured Amazon (AWS) S3 storage account, automatically or by batch selection.
Wal33D / Itchio DownloaderDownload free games from itch.io programmatically — no Puppeteer, no API key required. Supports HTML5 web games, platform selection, batch downloads.
jys125773 / React Native Tree Selectreact-native tree select ,suport single and multiple,support batch selection
zalandoresearch / Batching BenchmarksThis project contains accompanying code for Zalando Team BART's (Batching Algorithms) publication Joint Order Selection, Allocation, Batching and Picking for Large Scale Warehouses.
Feng-Hong / DivBS[ICML 2024] PyTorch implementation for "Diversified Batch Selection for Training Acceleration"
Tirth8038 / Multiclass Image Classification The main aim of the project is to scan the X-rays of human lungs and classify them into 3 given categories like healthy patients, patients with pre-existing conditions, and serious patients who need immediate attention using Convolutional Neural Network. The provided dataset of Grayscale Human Lungs X-ray is in the form of a numpy array and has dimensions of (13260, 64, 64, 1). Similarly, the corresponding labels of X-ray images are of size (13260, 2) with classes (0) if the patient is healthy, (1) if patient has pre-existing conditions or (2) if patient has Effusion/Mass in the lungs. During data exploration, I found that the class labels are highly imbalanced. Thus, for handling such imbalanced class labels, I used Data augmentation techniques such as horizontal & vertical flips, rotation, altering brightness and height & width shift to increase the number of training images to prevent overfitting problem. After preprocessing the data, the dimension of the dataset is (31574, 64, 64, 1). For Model Selection, I built 4 architectures of CNN Model similar to the architecture of LeNet-5, VGGNet, AlexNet with various Conv2D layers followed by MaxPooling2D layers and fitted them with different epochs, batch size and different optimizer learning rate. Moreover, I also built a custom architecture with comparatively less complex structure than previous models. Further to avoid Overfitting, I also tried regularizing Kernel layer and Dense layer using Absolute Weight Regularizer(L1) and to restrict the bias in classification, I used Bias Regularizer in the Dense layer. In addition to this, I also tried applying Dropout with a 20% dropout rate during training and Early Stopping method for preventing overfitting and evaluated that Early Stopping gave better results than Dropout. For evaluation of models, I split the dataset into training,testing and validation split with (60,20,20) ratio and calculated Macro F1 Score , AUC Score on test data and using the Confusion Matrix, I calculated the accuracy by dividing the sum of diagonal elements by sum of all elements. In addition to this, I plotted training vs. validation loss and accuracy graphs to visualize the performance of models. Interestingly, the CNN model similar to VGGNet with 5 Conv2D and 3 MaxPooling layers and 2 Dense layers performed better than other architecture with Macro F1 score of 0.773 , AUC score of 0.911 and accuracy of 0.777.
abdul-karim-mia / Batch ArtBoard RenamerA comprehensive Adobe Illustrator script for batch renaming artboards with a variety of customization options, including individual artboard selection, prefixes, suffixes, numbering formats, and live preview functionality.
K3NK3 / ComfyUI K3NK ComfyUI NodesGrabs the last N frames from a directory as a single batch for video continuation. Supports optional frame stride to skip frames between selections, improving temporal consistency when using interpolated frames. Ideal for feeding WanVideo ClipVision Encode or other video processing nodes.
inetkachev / WSAFileTransferTransfer files to Windows Subsystem for Android (WSA) devices within the host system efficiently using this batch script. Automatically added to the context menu upon installation to the SendTo folder, it auto-detects devices and offers a smooth selection process. Customizable ADB path and user-friendly interface ensure seamless WSA file transfers
linhaojia13 / Tf Batch SelectionA re-impletion of paper: Online Batch Selection For Faster Training of Neural Networks
vinayak1 / GCBSGithub Repo for the ICML 2023 paper "Global Selection of Contrastive Batches via Optimization on Sample Permutations"
seconds-0 / Nsa VibeNative Sparse Attention (NSA) M0 steel-thread: SDPA-only, RoPE, CSR mapping, batched prefill, masked varlen SDPA, selection packing, tests and CI.
