open-edge-platform/anomalib
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
Loading star history...
Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
3 releases in the last 90 days, the latest 20 days ago, with 127 contributors on GitHub.
Gained 8% more stars in the last 90 days than in the 90 days before.
Use Cases & Benefits
- Provides a deep learning library for benchmarking, developing, and deploying state-of-the-art anomaly detection algorithms.
- Offers a modular API with extensive model implementations and OpenVINO export for accelerated inference on Intel hardware.
- Use for building and benchmarking visual anomaly detection models on image and video datasets with minimal boilerplate.
- Use for deploying lightweight anomaly detection models on edge devices using OpenVINO and Intel XPU acceleration.
- Use for managing experiments and hyperparameter optimization with integrated support for Weights & Biases and Comet.ml.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
A library for benchmarking, developing and deploying deep learning anomaly detection algorithms
🌟 Announcing v2.3.0 Release! 🌟
We're thrilled to announce the release of Anomalib v2.3.0, featuring new models and many quality-of-life improvements! Some of the highlights are: New models
- AnomalyDINO : A new anomaly detection model leveraging DINOv2 features.
- SuperSimpleNet : Updated to the latest version with improved performance.
New features
- PatchCore half-precision : Support for FP16 training, reducing memory usage.
- Barebones Engine mode : A simplified engine mode for lightweight workflows.
- Kaput dataset : A new dataset for anomaly detection benchmarking.
- XPU device support : Intel XPU support in TorchInferencer for accelerated inference.
Bug fixes
- Fixed PatchCore GPU memory bottleneck (kNN) during inference.
- Fixed
F1AdaptiveThresholdbug when no anomalous images are in the validation set.- Fixed support for non-square input images in Dinomaly model.
- Fixed INT8_PTQ and INT8_ACQ export issues.
- Improved Windows path compatibility.
- Many more code and documentation updates.
We value your input! Please share feedback via GitHub Issues or our Discussions
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
1.0K+55
Open issues
87+14
Compared with peers in Backend
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| anthropics/skills | 187.8K | +2.9K | 15 | — |
| DuarteSantos8/openGym | 19.7K | +2.4K | 60 | 2 days ago |
| shiyu-coder/Kronos | 47.0K | +2.3K | 16 | — |
| NationalSecurityAgency/ghidra | 98.2K | +2.3K | 64 | 14 days ago |
| microsoft/VibeVoice | 56.0K | +1.1K | 29 | — |
| open-edge-platform/anomalib | 6.3K | — | 64 | 20 days ago |
| Category median | 56.0K | +2.3K | 29 | — |
Discover Repositories
Search across tracked repositories by name or description