spektran-baselines-v0

spektran/spektran-baselines-v0

spektran-baselines-v0 is a tabular regression model from spektran. Use it for the tabular regression task on the model card, and read the license before you ship it in a product. It is set up for sklearn. The card lists the license as apache-2.0.

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.

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Updated Aug 12, 2026

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408 KB

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Hugging Face

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.npz213 KB · 51%

At a glance

Task: Tabular Regression
Library: sklearn
License: apache-2.0
Access: Public
Created: Aug 10, 2026
Updated: Aug 12, 2026
SHA: 4050605a

Datasets

spektran/spektran-ch4-v0

Model card

From the Hugging Face model README

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 — 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 — Spectral Denoising

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

T4 — WMS Concentration

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

T5 — Drift Compensation

Baseline	Type
Moving Average	Classical
TCN	Deep Learning

T6 — OOD Detection

Baseline	Type
PCA + Mahalanobis	Statistical

T7 — Cross-Modality (TDLAS → NDIR)

Baseline	Type
Ridge (Cross-Modality)	Linear

T8 — Multi-Species

Baseline	Type
Ridge (Multi-Species)	Linear

T9 — 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},
}