tbhugging/singleview_cnn_fourier
What this model does
Given a single 128×128 projection image from the M8 GummyBear Tomography dataset, the model predicts
- particle_x
- particle_y
- particle_z
coordinates of the embedded particle.
Architecture
The model uses Fourier pooling in place of conventional max-pooling layers as described in the M8 study (see https://github.com/tbgitoo/gummybear-tomography).
Single-view CNN + Fourier pooling particle localiser from the
gummybear-tomography Final Report M8 Step 3
(train → validation/test on (particle_x, particle_y, particle_z)).
- Hub: tbhugging/singleview_cnn_fourier
- Companion dataset: tbhugging/gummybear-tomography
- Runnable report: GummyBearTomography_Final_Report.ipynb (M8 Step 3)
- Source study checkpoint: checkpoints/m8/m08_train_val_test_xyz.pt (arch
fourieronly)
Training configuration
- Input field:
anomaly_ref - Normalisation:
per_image_zscore - Camera:
180.0° (single view) - Targets:
particle_x, particle_y, particle_z - Library class:
LocalizerSingleViewFourier - Trainable parameters:
31811 - Stage learning rate:
0.03
Evaluation Results
Structured scores for the Hub widget are declared in the YAML model-index / metrics metadata (Model Cards — Evaluation Results).
Testing Data
- Dataset: tbhugging/gummybear-tomography (config
m8_1) - Splits:
validation,test - Protocol: Final Report M8 Step 3 (single-view
180°,anomaly_ref,per_image_zscore, targetsparticle_x, particle_y, particle_z) - Source checkpoint: checkpoints/m8/m08_train_val_test_xyz.pt (arch
fourieronly)
Metrics
Reported error is Euclidean RMSE over particle (x,y,z):
d_i = ||pred_i - y_i||_2, then RMSE_total = sqrt(mean_i d_i^2).
This matches the Final Report M8 Step 3 bars (not element-wise MSE).
Hub metric id: rmse (display name RMSE_total (Euclidean xyz)).
Results
| Split | Metric | Value |
|---|---|---|
validation | RMSE_total (Euclidean xyz) | 2.275541 |
test | RMSE_total (Euclidean xyz) | 1.491224 |
Source: Final Report M8 Step 3.
Load
import torch
# libraries from https://github.com/tbgitoo/gummybear-tomography
from tomography_ml.studies.single_view_m8 import make_m8_single_view_model
from tomography_ml.localization.builders import materialize_lazy_modules
model = make_m8_single_view_model('fourier', n_outputs=3, device='cpu')
materialize_lazy_modules(model, torch.zeros(1, 1, 128, 128))
state = torch.load('pytorch_model.bin', map_location='cpu', weights_only=True)
model.load_state_dict(state)
model.eval()
Do not treat this checkpoint as multi-view (M9) or multi-illumination (M10).
Inference
For an example with worked download, model instanciation and inference, see: 11_1_test_singleview_cnn_fourier.ipynb in the gummybear-tomography repository.