| Name | Version | Summary | date |
| spox |
0.12.1 |
A framework for constructing ONNX computational graphs. |
2024-06-18 10:54:17 |
| tisc |
0.1.1 |
Simple model creation and training framework for time series classification in Pytorch |
2024-06-15 10:17:08 |
| SciQLop |
0.8.0 |
An ergonomic and efficient application to browse and label in situ plasma measurements from multi-mission satellite data. |
2024-06-11 17:30:31 |
| jadbio |
1.2.15 |
A python wrapper for the public JADBio REST API. |
2024-06-05 09:13:48 |
| tdw |
1.13.0.0 |
3D simulation environment |
2024-05-31 19:20:52 |
| ai-helpers-pyspark-utils |
0.1.0a3 |
Common pyspark utils |
2024-05-30 13:12:50 |
| ai-helpers-pytorch-utils |
0.1.0a1 |
Common PyTorch utils |
2024-05-30 12:10:51 |
| ai-helpers-jax-utils |
0.1.0a1 |
Common jax utils |
2024-05-30 12:09:20 |
| mlconcepts |
0.0.1a5 |
A small library implementing several interpretable machine learning algorithms based on FCA. |
2024-05-29 10:12:44 |
| sumonet |
0.1.7 |
Package Description |
2024-05-22 15:51:19 |
| jury |
2.3.1 |
Evaluation toolkit for neural language generation. |
2024-05-20 08:28:12 |
| octoflow-plugins |
0.0.5 |
Streamlining machine learning tracking for seamless experiment management. |
2024-05-07 01:43:14 |
| LugandaOCR |
1.0.0 |
Optical Character Recognition (OCR) for Luganda language text images using TensorFlow |
2024-05-06 11:25:32 |
| bytetracker-gml |
0.3.2 |
Packaged version of the ByteTrack repository |
2024-05-03 23:32:26 |
| pyppbox-ultralytics |
8.1.48 |
Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification. |
2024-04-30 11:41:27 |
| iog-sdk |
0.0.1 |
IOG SDK - Internet of GPUs - IO.net |
2024-04-29 22:27:48 |
| topsearch |
0.0.3 |
A Python package for topographical analysis of machine learning models and physical systems |
2024-04-24 10:34:46 |
| phenocv |
0.1.4 |
Rice High Throughput Phenotyping Computer Vision Toolkit |
2024-04-23 05:27:13 |
| intel-ai-safety |
0.0.0 |
Explainable AI Tooling (XAI). XAI is used to discover and explain a model's prediction in a way that is interpretable to the user. Relevant information in the dataset, featureset, and model's algorithms are exposed. |
2024-04-23 02:52:49 |
| ultralytics-nogui |
8.2.2 |
Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification. |
2024-04-22 20:51:35 |