prismpyp eval2d¶
Purpose¶
Evaluate and visualize 2D embeddings or model features from trained SimSiam networks.
This command can:
- Run 2D embedding evaluation on real-space or Fourier-space inputs
- Optionally resume from precomputed embeddings
- Perform clustering and UMAP dimensionality reduction
- Save visualizations, plots, and
.webpimage thumbnails
Usage¶
usage: prismpyp eval2d [-h] [--output-path DIR] [--metadata-path METADATA_PATH] [--embedding-path [EMBEDDING_PATH]] [-a ARCH] [-j N] [--epochs N]
[--start-epoch N] [-b N] [--lr LR] [--momentum M] [--wd W] [-p N] [--resume PATH] [--feature-extractor-weights PATH]
[--world-size WORLD_SIZE] [--rank RANK] [--dist-url DIST_URL] [--dist-backend DIST_BACKEND] [--seed SEED] [--gpu GPU]
[--multiprocessing-distributed] [--dim DIM] [--pred-dim PRED_DIM] [--fix-pred-lr] [--use-fft] [--downsample DOWNSAMPLE]
[--pixel-size PIXEL_SIZE] [--size SIZE] [--evaluate] [--n-clusters N_CLUSTERS] [--num-neighbors NUM_NEIGHBORS]
[--min-dist-umap MIN_DIST_UMAP] [--n-components N_COMPONENTS] [--nextpyp-preproc NEXTPYP_PREPROC] [--zip-images]
Named Arguments¶
Commonly Changed¶
| Argument | Description | Default |
|---|---|---|
--output-path DIR |
Path to output directory | — |
--metadata-path METADATA_PATH |
Path to metadata file | — |
--embedding-path EMBEDDING_PATH |
Optional path to precomputed embeddings | — |
--feature-extractor-weights PATH |
Path to pre-trained feature extractor weights | none |
--dim DIM |
Feature dimension | 512 |
--pred-dim PRED_DIM |
Hidden dimension of the predictor | 256 |
--fix-pred-lr |
Fix learning rate for the predictor | True |
--use-fft |
Use FFT of the image as input | False |
--evaluate |
Evaluate model on validation set | True |
--n-clusters N_CLUSTERS |
Number of clusters for KMeans | — |
--num-neighbors NUM_NEIGHBORS |
Number of neighbors for UMAP | — |
--min-dist-umap MIN_DIST_UMAP |
Minimum distance for UMAP | 0 |
--n-components N_COMPONENTS |
Number of UMAP components | — |
Available Architectures:
resnet18, resnet34, resnet50