Galaxy Bar Detection Resnet50
CNNs that detect stellar bars in simulated disc galaxies — and, in the final version, measure bar strength as a continuous quantity.
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- Type
- Other
- Taille du fichier
- 270 MB
- Version
- vRevision cd26e7d
- Mis à jour
- 27 oct. 2026
- Téléchargements
- 29
- Source
- Hugging Face
Description de la source
Galaxy Bar Detection — ResNet50 (IITK Astronomy Project)
CNNs that detect stellar bars in simulated disc galaxies — and, in the final version, measure bar strength as a continuous quantity.
⭐ Final model: regression_resnet50.weights.h5 — 96.0% accuracy
Instead of binary bar/no-bar labels, the final model regresses the physical A2/A0 Fourier bar-strength directly from a 224x224 grayscale stellar density image. Classification = predicted A2/A0 >= 0.19.
| Metric (450-img test set, 18 unseen snapshots) | Value |
|---|---|
| Classification accuracy | 96.0% |
| Bar recall | 92.0% |
| Strength MAE | 0.027 (A2/A0 units, range 0-0.28) |
Why regression beat binary classification (86.4% -> 96.0% on the SAME test):
it unlocked ~60 mid-strength galaxies (0.10 < A2/A0 < 0.22) that binary
labeling had to discard, curing the classifier's "boundary blindness" around
its own decision threshold. See pred_vs_true_final.png — the model acts as
a bar-strength measuring instrument.
Loading (weights-only, due to a Keras serialization bug with the full model)
from tensorflow import keras
from tensorflow.keras import layers
base = keras.applications.ResNet50(weights=None, include_top=False, input_shape=(224,224,3))
inp = keras.Input((224,224,3))
x = keras.Sequential([layers.Identity()])(inp) # placeholder (training had augmentation here)
x = keras.applications.resnet50.preprocess_input(x)
x = base(x, training=False)
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dropout(0.3)(x)
out = layers.Dense(1, activation="sigmoid")(x) # output * 0.30 = A2/A0
model = keras.Model(inp, out)
model.load_weights("regression_resnet50.weights.h5")
Input: 224x224 grayscale stellar density map (log10, clipped 1e7-1e9, normalized 0-1, replicated to 3 channels), 40 kpc field of view, galaxy centered and inclination <= 40 deg. Output x 0.30 = predicted A2/A0;
= 0.19 = barred.
Earlier binary models (the journey)
bar_resnet50_v{1,2,3}.keras — binary classifiers documenting the iterative
arc: v1 76.5% (tilts to 60 deg, label noise) -> v2 86.8% (tilt cap 40 deg) ->
v3 86.4% w/ AUC 0.944 (122 snapshots). With test-time augmentation + 3-seed
ensembling v3 reached 91.8%. A morpho-kinematic variant (velocity channels)
was tested and did NOT help (83.3%) — morphology suffices at <= 40 deg.
Architecture comparison on regression: ResNet50 96.0% >> ConvNeXt-Tiny 91.6%
EfficientNetV2-B0 84.0%; ensembles diluted the champion.
Labels are physical, not manual: each simulation snapshot's A2/A0 Fourier bar strength (from an N-body GADGET simulation, 502 snapshots) labels every image rendered from it. Data always split by snapshot to prevent leakage.
Code, dataset pipeline, full analysis: https://github.com/GuptaOum/IITK_astronomy_project
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2 extraits--- license: mit tags: - image-classification - regression - astronomy - galaxy-morphology - tensorflow - resnet50 - transfer-learning library_name: keras --- # Galaxy Bar Detection — ResNet50 (IITK Astronomy Project) CNNs that detect **stellar bars** in simulated disc galaxies — and, in the final version, **measure bar strength** as a continuous quantity. ## ⭐ Final model: `regression_resnet50.weights.h5` — 96.0% accuracy Instead of binary bar/no-bar labels, the final model regresses the physical **A2/A0 Fourier bar-strength** directly from a 224x224 grayscale stellar density image. Classification = predicted A2/A0 >= 0.19. | Metric (450-img test set, 18 unseen snapshots) | Value | |---|---| | Classification accuracy | **96.0%** | | Bar recall | 92.0% | | Strength MAE | **0.027** (A2/A0 units, range 0-0.28) | Why regression beat binary classification (86.4% -> 96.0% on the SAME test): it unlocked ~60 mid-strength galaxies (0.10 < A2/A0 < 0.22) that binary labeling had to discard, curing the classifier's "boundary blindness" around its own decision threshold. See `pred_vs_true_final.png` — the model acts as a bar-strength measuring instrument. ### Loading (weights-only, due to a Keras serialization bug with the full model) ```python from tensorflow import keras from tensorflow.keras import layers base = keras.applications.ResNet50(weights=None, include_top=False, input_shape=(224,224,3)) inp = keras.Input((224,224,3)) x = keras.Sequential([layers.Identity()])(inp) # placeholder (training had augmentation here) x = keras.applications.resnet50.preprocess_input(x) x = base(x, training=False) x = layers.GlobalAveragePooling2D()(x) x = layers.Dropout(0.3)(x) out = layers.Dense(1, activation="sigmoid")(x) # output * 0.30 = A2/A0 model = keras.Model(inp, out) model.load_weights("regression_resnet50.weights.h5") ``` Input: 224x224 grayscale stellar density map (log10, clipped 1e7-1e9, normalized 0-1, replicated to 3 channels), 40 kpc field of view, galaxy centered and inclination <= 40 deg. Output x 0.30 = predicted A2/A0; >= 0.19 = barred. ## Earlier binary models (the journey) `bar_resnet50_v{1,2,3}.keras` — binary classifiers documenting the iterative arc: v1 76.5% (tilts to 60 deg, label noise) -> v2 86.8% (tilt cap 40 deg) -> v3 86.4% w/ AUC 0.944 (122 snapshots). With test-time augmentation + 3-seed ensembling v3 reached 91.8%. A morpho-kinemati...
Source context: 29 downloads · 0 likes · Pipeline image-classification · Library keras · Repo kjfk/galaxy-bar-detection-resnet50
