| --- |
| library_name: pytorch |
| pipeline_tag: image-to-image |
| tags: |
| - astronomy |
| - astrophotography |
| - image-denoising |
| - star-removal |
| - pytorch |
| --- |
| |
| # AstroStarDetect |
|
|
| PyTorch checkpoints and deployment code for astronomical image denoising and star removal. |
|
|
| Source repository: <https://github.com/CHMimilanlan/AstroColorProcess> |
|
|
| ## Included models |
|
|
| | Model | Parameters | Parameter count (B) | Checkpoint size | Purpose | |
| | --- | ---: | ---: | ---: | --- | |
| | Cosmic Clarity AI 3.6 wrapper | 2,945,283 | 0.002945B | 33.77 MiB | Astronomical image denoising | |
| | StarNet RGB generator | 54,420,483 | 0.054420B | 207.67 MiB | Star removal from RGB TIFF images | |
| | StarNet greyscale generator | 54,414,337 | 0.054414B | 207.64 MiB | Star removal from greyscale TIFF images | |
| | **All checkpoints** | **111,780,103** | **0.111780B** | **449.08 MiB** | Stored models; RGB and greyscale variants are not normally loaded together | |
|
|
| FP32 parameter memory is 11.24 MiB for the denoiser, 207.60 MiB for StarNet RGB, and |
| 207.57 MiB for StarNet greyscale. At batch 1 and 256×256 input, profiled FP32 tensor-memory |
| peaks are approximately 361.67 MiB, 356.33 MiB, and 355.31 MiB respectively. These are |
| compute-graph estimates and exclude the CUDA context, cuDNN workspace, and allocator cache. |
| The measurements were collected in a CPU-only PyTorch environment, so they are not direct CUDA |
| `max_memory_allocated` measurements. See the GitHub README for sizing methodology, |
| 512×512 estimates, and recommended GPU capacities. |
|
|
| ## Quick start |
|
|
| ```bash |
| git clone https://github.com/CHMimilanlan/AstroColorProcess.git |
| cd AstroColorProcess |
| python -m pip install -r requirements.txt |
| python download_models.py |
| ``` |
|
|
| Denoise an image: |
|
|
| ```bash |
| python StarDenoise/denoise.py input.tif output.tif --device auto --strength 0.85 |
| ``` |
|
|
| Remove stars from an RGB TIFF image: |
|
|
| ```bash |
| python StarNetPyTorch/starnet_v1_pytorch_transform.py input.tif output_starless.tif --mode RGB |
| ``` |
|
|
| The download helper places every checkpoint at the default location expected by the inference scripts. |
| See the [GitHub README](https://github.com/CHMimilanlan/AstroColorProcess#readme) for Windows/Conda instructions, parameters, checksums, offline deployment, and licensing notes. |
|
|
| ## Attribution and licensing |
|
|
| `StarDenoise` wraps the Cosmic Clarity AI 3.6 PyTorch model and retains its upstream MIT license text. |
| `StarNetPyTorch` is a PyTorch translation and inference example for the StarNet architecture. |
| Third-party checkpoints and examples remain subject to their original upstream terms; verify those terms before commercial use. |
|
|
