Condensatenet v1

--- language: en library_name: pytorch license: mit metrics: - type: dice value: 0.5164 - type: f1 value: 0.5164 - type: iou value: 0.3505 tags: - image-segmentation - microscopy - condensate-detection - cellpose-flows - efficientnetv2 --- # CondensateNet v1 Semantic segmentation model for detecting biomolecular condensates (puncta / stress granules) in fluorescence microscopy images. ## Model Description - **Architecture**: EfficientNetV2-S encoder with style-modulated Feature Pyramid Network (FPN) and dual output heads (binary mask + Cellpose-style flow fields) - **Parameters**: 22.2M - **Input**: Single-channel grayscale microscopy tile (320x320), percentile-normalized to [0, 1] - **Output**: Binary segmentation mask logits + 2-channel flow field ## Training - **Data**: 245 validated image-mask pairs from 14 experiment types (fluorescence microscopy of various condensate-forming proteins) - **Split**: 158 train / 26 val (stratified by experiment, with caps on overrepresented experiments) - **Loss**: Focal + Tversky + flow MSE + differentiable calibration (soft-binned ECE) - **Optimizer**: AdamW with linear warmup (5 epochs) + cosine annealing (120 epochs total, early stopped at epoch 94) - **Augmentation**: Experiment-aware augmentation tiers (light/medium/heavy) with strong contrast scaling, CLAHE, intensity scaling (0.3x-2.0x), geometric transforms ### Metrics (validation set) | Metric | Value | |--------|-------| | Dice | 0.5164 | | F1 | 0.5164 | | IoU | 0.3505 | ### Inference metrics (full validation set, per-tile with threshold search) | Metric | Mean | Std | |--------|------|-----| | Dice | 0.534 | 0.155 | | Precision | 0.517 | 0.151 | | Recall | 0.588 | 0.203 | ## Usage ```python from condensatenet import CondensateNetPipeline # Load from HuggingFace pipeline = CondensateNetPipeline.from_pretrained("rajlab/condensatenet-v1") # Segment an image instances = pipeline.segment(image) print(f"Found {instances.max()} condensates") # Or load from a local download pipeline = CondensateNetPipeline.from_local("/path/to/model") ``` ## Intended Use Research tool for detecting biomolecular condensates in fluorescence microscopy. Not intended for clinical or diagnostic use. ## Training Runs This model was iteratively improved over 5 training runs: | Run | Changes | Val Dice | |-----|---------|----------| | 1 | Baseline | 0.503 | | 2 | +c...

Source context: 12 downloads · 0 likes · Pipeline image-segmentation · Library pytorch · Repo rajlab/condensatenet-v1