An Agnostic Object Detection Framework
IceVision is the first agnostic computer vision framework to offer a curated collection with hundreds of high-quality pre-trained models from torchvision, MMLabs, and soon Pytorch Image Models. It orchestrates the end-to-end deep learning workflow allowing to train networks with easy-to-use robust high-performance libraries such as Pytorch-Lightning and Fastai
IceVision Unique Features:
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Data curation/cleaning with auto-fix
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Access to an exploratory data analysis dashboard
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Pluggable transforms for better model generalization
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Access to hundreds of neural net models
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Access to multiple training loop libraries
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Multi-task training to efficiently combine object detection, segmentation, and classification models
Installation
pip install icevision[all]
For more installation options, check our docs.
Important: We currently only support Linux/MacOS.
Quick Example: How to train the Fridge Objects Dataset
Happy Learning!
If you need any assistance, feel free to:
2 maintainers
Metadata
2 maintainers
Release files for icevision 0.12.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| icevision-0.12.0.tar.gz | 134.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| icevision-0.12.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 397.5 kB
Release files / icevision-0.12.0.tar.gz
| Download URL | icevision-0.12.0.tar.gz |
|---|---|
| Size | 134.8 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / icevision-0.12.0-py3-none-any.whl
| Download URL | icevision-0.12.0-py3-none-any.whl |
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| Size | 262.7 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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