Top 5 Jupyter Notebook Imagenet Projects
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super-gradients
Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
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tensorflow-image-models
TensorFlow port of PyTorch Image Models (timm) - image models with pretrained weights.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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conformal_classification
Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Most computer vision models are trained to predict on a preset list of label classes. In object detection, for instance, many of the most popular models like YOLOv8 and YOLO-NAS are pretrained with the classes from the MS COCO dataset. If you download the weights checkpoints for these models and run prediction on your dataset, you will generate object detection bounding boxes for the 80 COCO classes.
Jupyter Notebook Imagenet related posts
Index
What are some of the best open-source Imagenet projects in Jupyter Notebook? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | super-gradients | 4,332 |
| 2 | tensorflow-image-models | 280 |
| 3 | ImageNetV2 | 224 |
| 4 | conformal_classification | 211 |
| 5 | nested-transformer | 190 |
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