anomalib
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open-edge-platform
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
### Describe the bug I am attempting to train Anomalib models using an Intel XPU. Following the instructions in the README, I have installed a PyTorch version compatible with Intel...
## 📝 Description - This PR will add the GLASS model for synthesis of anomalies on a global and local level. - 🛠️ Fixes #2619 ## ✨ Changes Select what...
### Describe the bug When using multiple GPUs to train a model, the checkpoint files saved by the model will lose information ### Dataset N/A ### Model PatchCore ### Steps...
### Describe the bug I am copy pasting the code from `examples/api/03_models/padim.py` and trying to run it. When fitting the model, there is an IndexError somewhere in the implementation of...
in anomalib PaDiM, model.image_threshold = 88.8 for the best F1 score. but I want the recall = 1 even if I lose a little bit in precision, so I want...
### Describe the bug When I train a RD model with anomalib 2.0.0 on a machine with mulitple GPUs, I get this warning message right before the first epoch starts:...
### Describe the bug Hello all, I was excited for Anomalib v2 release for several months now and I have run into the dreaded FATAL FIPS SELFTEST FAILURE. I am...
## 📝 Description #### ✅ Fixes and Refactors - **Fixed #2592** by **refactoring the access pattern** of `ImageBatch` from **subscriptable access** (e.g., `batch['image']`) to **attribute-style access** (e.g., `batch.image`). --- ####...
### User Story I want to implement a new anomaly detection model - GLASS based on [this paper](https://arxiv.org/abs/2407.09359). This model combines the use global and local anomaly synthesis to improve...
Hello, I previously used Anomalib v0.7 in detection mode, where I could retrieve a box score for each bounding box, making it easier to apply a threshold later. However, in...
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An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.