[torchood]: pytorch implementation for universal out of distribution methods
pip install torchood
Support Methods
| Support Methods | Source |
|---|---|
| Robust Classification with Convolutional Prototype Learning (Prototype Networks) | CVPR 2018: Rbust classification with convolutional prototype learning |
| Predictive Uncertainty Estimation via Prior Networks(Prior Networks) | NeurIPS 2018: Predictive Uncertainty Estimation via Prior Networks |
| Evidential Deep Learning to Quantify Classification Uncertainty (EDL) | NeurIPS 2018: Evidential Deep Learning to Quantify Classification Uncertainty |
| Posterior Network (PostNet) | NeurIPS 2020: Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts |
| Evidential Neural Network (ENN) | NeurIPS 2018: Evidential Deep Learning to Quantify Classification Uncertainty |
| Evidence Reconciled Neural Network(ERNN) | MICCAI 2023: Evidence Reconciled Neural Network for Out-of-Distribution Detection in Medical Images |
| Redundancy Removing Evidential Neural Network(R2ENN) | - |
Usage
pip install torchood
import torchood
class UserDefineModel(nn.Module):
...
classifier = UserDefineModel(...)
classifier = torchood.EvidenceNeuralNetwork(classifier)
Train
for data, label in train_loader:
# transform data & label to correct device
# ...
_, evidence, _ = classifier(data)
loss = classifier.criterion(evidence, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
Infer
prob, uncertainty = classifier.predict(inputs)
1 maintainer
Metadata
1 maintainer
Release files for torchood 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| torchood-0.0.1.tar.gz | 7.6 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| torchood-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 15.8 kB
Release files / torchood-0.0.1.tar.gz
| Download URL | torchood-0.0.1.tar.gz |
|---|---|
| Size | 7.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
2b254a7cb001bb822eff11d5e2fc0512a5e665b17db2a283493cc188b02be1ff
|
|
BLAKE2b-256 checksum How to use checksums |
067adc31ca4fb7344d0882e5e9b14f516122fbc8f50de6ed2f789e36619384f1
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.0.1 CPython/3.11.5
|
Release files / torchood-0.0.1-py3-none-any.whl
| Download URL | torchood-0.0.1-py3-none-any.whl |
|---|---|
| Size | 8.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
6e6ef8068eb38f9887aa60599c1472fce0533f5cd6db5c9694329ccdf2f8f32e
|
|
BLAKE2b-256 checksum How to use checksums |
cf3574d0c976886aa16f011313e43c3cad6cb06254eadfb69a10b7e98cdf32fb
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.0.1 CPython/3.11.5
|
Security for torchood 0.0.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
