Spaces:
Runtime error
Runtime error
merge histories
Browse files- .gitattributes +1 -1
- .github/workflows/spaces.yml +1 -1
- README.md +5 -1
.gitattributes
CHANGED
|
@@ -31,4 +31,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 31 |
*.xz filter=lfs diff=lfs merge=lfs -text
|
| 32 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 33 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 34 |
-
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
| 31 |
*.xz filter=lfs diff=lfs merge=lfs -text
|
| 32 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 33 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 34 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
.github/workflows/spaces.yml
CHANGED
|
@@ -16,4 +16,4 @@ jobs:
|
|
| 16 |
- name: Push to hub
|
| 17 |
env:
|
| 18 |
HF_TOKEN: ${{ secrets.HF_TOKEN }}
|
| 19 |
-
run: git push https://edadaltocg:[email protected]/spaces/edadaltocg/ood-detection main
|
|
|
|
| 16 |
- name: Push to hub
|
| 17 |
env:
|
| 18 |
HF_TOKEN: ${{ secrets.HF_TOKEN }}
|
| 19 |
+
run: git push https://edadaltocg:[email protected]/spaces/edadaltocg/ood-detection main
|
README.md
CHANGED
|
@@ -10,6 +10,7 @@ pinned: true
|
|
| 10 |
license: mit
|
| 11 |
---
|
| 12 |
|
|
|
|
| 13 |
# OOD Detection Demo
|
| 14 |
|
| 15 |
Out-of-distribution (OOD) detection is an essential safety measure for machine learning models. This app demonstrates how these methods can be useful in determining wether the inputs of a ResNet-50 model trained on ImageNet-1K can be trusted by the model.
|
|
@@ -36,4 +37,7 @@ python app.py
|
|
| 36 |
|
| 37 |
- [ ] [Mahalanobis Distance](https://arxiv.org/abs/1807.03888)
|
| 38 |
- [x] [Maximum Softmax Probability](https://arxiv.org/abs/1610.02136)
|
| 39 |
-
- [x] [Energy Based Out-of-Distribution Detection](https://arxiv.org/abs/2010.03759)
|
|
|
|
|
|
|
|
|
|
|
|
| 10 |
license: mit
|
| 11 |
---
|
| 12 |
|
| 13 |
+
<<<<<<< HEAD
|
| 14 |
# OOD Detection Demo
|
| 15 |
|
| 16 |
Out-of-distribution (OOD) detection is an essential safety measure for machine learning models. This app demonstrates how these methods can be useful in determining wether the inputs of a ResNet-50 model trained on ImageNet-1K can be trusted by the model.
|
|
|
|
| 37 |
|
| 38 |
- [ ] [Mahalanobis Distance](https://arxiv.org/abs/1807.03888)
|
| 39 |
- [x] [Maximum Softmax Probability](https://arxiv.org/abs/1610.02136)
|
| 40 |
+
- [x] [Energy Based Out-of-Distribution Detection](https://arxiv.org/abs/2010.03759)
|
| 41 |
+
=======
|
| 42 |
+
# OOD Detection
|
| 43 |
+
>>>>>>> 45fabfa417588e2aeb366695552bd6c8de1e73cc
|