ttach
Images test time augmentation with PyTorch.
Decision gist · record as of 2026-08-14
No—the package is abandoned (last release 2020-07-09, last commit 2023-07-28) with no maintenance or compatibility updates. While it has low install friction and permissive licensing, the lack of active maintenance poses a significant risk for compatibility with current PyTorch and Python versions. For active TTA support, consider maintained alternatives or implementing TTA directly in your training framework.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyTorch to be installed separately; the package itself has no runtime dependencies but will not function without a PyTorch model to wrap.
- Low friction install with no runtime dependencies.
- However, the package is abandoned—last release was 2020-07-09 and last commit 2023-07-28—so expect no maintenance, bug fixes, or compatibility updates for newer PyTorch or Python versions.
License · maintenance · safety
MIT (permissive) — MIT license is permissive and places no restrictions on use, modification, or distribution in commercial or private projects.
last release 2020-07-09 (2227 days) · last repo commit 2023-07-28 · 1,030 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 140,340 downloads/mo, #11,274 on PyPI
Alternatives
Verify before relying
pip install ttach
import ttach as tta
model = ... # your trained PyTorch model
tta_model = tta.SegmentationTTAWrapper(model, tta.aliases.d4_transform(), merge_mode='mean')
predictions = tta_model(images)- Compatibility with current PyTorch versions (package last updated 2020-07-09).
- Whether the package works with modern Python versions beyond 3.0 despite the broad classifier.
- Performance impact of test-time augmentation on inference latency for typical model sizes.
What it is and what it does
TTAch is a PyTorch wrapper library that implements test-time augmentation (TTA)—a technique where multiple augmented versions of an input image are passed through a trained model, and the resulting predictions are merged (averaged, max-pooled, etc.) to produce a final output. Instead of showing a model one clean image, TTA shows it many transformed versions and combines their predictions, often improving accuracy and robustness at the cost of inference time.
The library provides pre-built wrappers for segmentation, classification, and keypoint detection models, along with a composable transform system (flips, rotations, scaling, crops, etc.) and multiple merge strategies (mean, geometric mean, max, min, temperature sharpening). You can wrap an existing model in one line and immediately use TTA, or build custom augmentation pipelines for specialized workflows.
Use it for
- Improve segmentation model accuracy on medical images by averaging predictions across rotations and flips.
- Boost classification confidence on edge cases by merging predictions from multiple scaled and cropped versions of the input.
- Refine keypoint detection by applying TTA with geometric transformations and reversing them before merging.
- Ensemble inference without training multiple models—apply TTA to a single model for a quick accuracy boost.
- Benchmark model robustness by testing how predictions vary across different augmentations.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No—the package is abandoned (last release 2020-07-09, last commit 2023-07-28) with no maintenance or compatibility updates.
While it has low install friction and permissive licensing, the lack of active maintenance poses a significant risk for compatibility with current PyTorch and Python versions. For active TTA support, consider maintained alternatives or implementing TTA directly in your training framework.
Install
ttach on PyPI
Before you install
Low friction install with no runtime dependencies. However, the package is abandoned—last release was 2020-07-09 and last commit 2023-07-28—so expect no maintenance, bug fixes, or compatibility updates for newer PyTorch or Python versions.
Requires PyTorch to be installed separately; the package itself has no runtime dependencies but will not function without a PyTorch model to wrap.
License in practice
MIT license is permissive and places no restrictions on use, modification, or distribution in commercial or private projects.
Quickstart
pip install ttach
import ttach as tta
model = ... # your trained PyTorch model
tta_model = tta.SegmentationTTAWrapper(model, tta.aliases.d4_transform(), merge_mode='mean')
predictions = tta_model(images)Verify before relying
- Compatibility with current PyTorch versions (package last updated 2020-07-09).
- Whether the package works with modern Python versions beyond 3.0 despite the broad classifier.
- Performance impact of test-time augmentation on inference latency for typical model sizes.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.0.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Abandoned 2,227 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 140,340 / month, #11,274 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPy |
Evidence: ttach-0.0.3-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “test time augmentation pytorch”
- ttachWraps PyTorch models to apply test-time augmentation—running…
- torch-audiomentationsProvides PyTorch-native audio data augmentation transforms that run…
- batchgeneratorsv2Reimplemented data augmentation transforms for deep learning,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also batchgeneratorsv2 · imgaug · torch-audiomentations · batchgenerators · albumentations · augmax · pytorch_revgrad · torch · nlpaug · imgviz