SPEKTRAN Baseline Checkpoints v0.6.0

Pre-trained baseline model weights for the SPEKTRAN optical gas sensing benchmark. 25 baselines across 5 modalities (TDLAS, NDIR, CRDS, FTIR, DOAS) and 9 benchmark tasks.

Available Pre-trained Weights

Checkpoint Task Modality Model Key Metric
ridge-t1-da/ T1 Concentration TDLAS DA Ridge 2.80 ppm MAE
cnn1d-t1-da/ T1 Concentration TDLAS DA CNN1D 16.79 ppm MAE
ridge-t4-wms/ T4 WMS Concentration TDLAS WMS Ridge 12.46 ppm MAE
ridge-t9-temperature/ T9 Temperature TDLAS DA Ridge 7.03 K MAE

All 25 Registered Baselines

Train any baseline from scratch with one command:

pip install spektran[dev]
spektran train --baseline ridge        # T1 concentration
spektran train --baseline cnn1d        # T1 CNN
spektran train --baseline transformer  # T1 Transformer
spektran train --baseline ridge_crds   # T1 CRDS
spektran train --baseline ridge_ftir   # T1 FTIR
spektran train --baseline ridge_doas   # T1 DOAS
spektran list baselines --json         # List all 25

T1 β€” Concentration Regression (TDLAS DA)

Baseline Type Params
Ridge Regression Linear ~201
1D CNN Deep Learning ~50K
Patchified Transformer Deep Learning ~100K
Random Forest / GBR Ensemble ~1M
MLP (BPNN) Deep Learning ~50K
BiLSTM RNN ~60K
CNN-LSTM-Attention Hybrid ~80K
PINN Physics-Informed ~50K
SpektralNet TDLAS-native ~45K
Voigt Fit (LM) Classical physics 0 (no training)

T2 β€” Spectral Denoising

Baseline Type
Wing-Anchored Polynomial Classical
1D U-Net Deep Learning
LSTM-DAE Autoencoder

T4 β€” WMS Concentration

Baseline Type
Ridge (WMS) Linear
CNN (WMS) Deep Learning
Transformer (WMS) Deep Learning

T5 β€” Drift Compensation

Baseline Type
Moving Average Classical
TCN Deep Learning

T6 β€” OOD Detection

Baseline Type
PCA + Mahalanobis Statistical

T7 β€” Cross-Modality (TDLAS β†’ NDIR)

Baseline Type
Ridge (Cross-Modality) Linear

T8 β€” Multi-Species

Baseline Type
Ridge (Multi-Species) Linear

T9 β€” Temperature Regression

Baseline Type
Ridge (Temperature) Linear

New Modality Baselines (v0.6.0)

Baseline Modality Type
Ridge (CRDS) CRDS Linear
Ridge (FTIR) FTIR Linear
Ridge (DOAS) DOAS Linear

Quick Start

Load Pre-trained Ridge

import numpy as np
from huggingface_hub import hf_hub_download

# Download weights
path = hf_hub_download("spektran/spektran-baselines-v0", "ridge-t1-da/weights.npz")
data = np.load(path)

# Predict concentration from raw scan
X_scaled = (raw_scan - data["scaler_mean"]) / data["scaler_scale"]
concentration_ppm = X_scaled @ data["coef"] + data["intercept"][0]

Load Pre-trained CNN

import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download

norm_path = hf_hub_download("spektran/spektran-baselines-v0", "cnn1d-t1-da/normalization.npz")
model_path = hf_hub_download("spektran/spektran-baselines-v0", "cnn1d-t1-da/model.pt")

norm = np.load(norm_path)
state = torch.load(model_path, weights_only=True)

model = nn.Sequential(
    nn.Conv1d(1, 16, 15, stride=2, padding=7), nn.ReLU(),
    nn.Conv1d(16, 32, 9, stride=2, padding=4), nn.ReLU(),
    nn.Conv1d(32, 64, 5, stride=2, padding=2), nn.ReLU(),
    nn.AdaptiveAvgPool1d(8), nn.Flatten(),
    nn.Linear(64 * 8, 64), nn.ReLU(), nn.Linear(64, 1),
)
model.load_state_dict(state)

Train Any Baseline

pip install spektran[dev] scikit-learn torch
spektran train --baseline ridge --json    # Auto-generates data + trains + evaluates
spektran train --baseline cnn1d --json
spektran train --baseline ridge_crds --json

For AI Agents

import subprocess, json

# Discover baselines
result = subprocess.run(["spektran", "list", "baselines", "--json"],
                       capture_output=True, text=True)
baselines = json.loads(result.stdout)

# Train and evaluate
result = subprocess.run(["spektran", "train", "--baseline", "ridge", "--json"],
                       capture_output=True, text=True)
scores = json.loads(result.stdout)

Citation

@software{spektran2026,
  title = {SPEKTRAN: Synthetic ML Training Data for Optical Gas Sensing},
  url = {https://github.com/spektran/spektran},
  doi = {10.5281/zenodo.21790394},
  version = {0.6.0},
  year = {2026},
}

Links

Downloads last month
-
Inference Providers NEW
This model isn't deployed by any Inference Provider. πŸ™‹ Ask for provider support

Dataset used to train spektran/spektran-baselines-v0