Celldino

LICENSE_CELLDINO_WEIGHTS

@@ -0,0 +1,124 @@
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README.md

@@ -1,5 +1,7 @@
 :new: [2025-08-14] *Please check out the more recent [DINOv3](https://github.com/facebookresearch/dinov3) effort continuing this line of work.*
 
+:new: [2025-11-21] *Added ChannelAgnostic-DINO code following [Scaling Channel-Adaptive Self-Supervised Learning](https://openreview.net/forum?id=pT8sgtRVAf), and Cell-DINO code [README](https://github.com/facebookresearch/dinov2/blob/main/README_CELL-DINO_AND_CHANNEL-DINO.md)*
+
 [2025-06-11] *Added dino.txt inference code, following [DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment](https://arxiv.org/abs/2412.16334).*
 
 [2023-10-26] *Added DINOv2 backbones with registers, following [Vision Transformers Need Registers](https://arxiv.org/abs/2309.16588).*

README_CELL-DINO_AND_CHANNEL-DINO.md

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+The contents of this repo, including the code and model weights, are intended for research use only. It is not for use in medical procedures, including any diagnostics, treatment, or curative applications. Do not use this model for any clinical purpose or as a substitute for professional medical judgement.
+
+
+# Scaling Channel-Adaptive Self-Supervised Learning (CHANNEL-DINO)
+
+ [[`Paper `](https://openreview.net/forum?id=pT8sgtRVAf))] [[`BibTeX`](#citing-channeladaptivedino-and-dinov2)]
+
+Alice V. De Lorenci, Seungeun Yi, Théo Moutakanni, Piotr Bojanowski, Camille Couprie, Juan C. Caicedo, Wolfgang M. Pernice,
+
+with special thanks to Elouan Gardes for his contributions to the codebase.
+
+ [README](https://github.com/facebookresearch/dinov2/blob/main/docs/README_CHANNEL_ADAPTIVE_DINO.md)
+
+
+
+# Cell-DINO: Unbiased Image-based Embeddings for Cell Fluorescent Microscopy (CELL-DINO)
+
+Théo Moutakanni, Camille Couprie, Seungeun Yi, Elouan Gardes, Piotr Bojanowski, Hugo Touvron, Michael Doron, Zitong S. Chen, Nikita Moshkov, Mathilde Caron, Armand Joulin,  Wolfgang M. Pernice, Juan C. Caicedo
+
+to appear soon.
+
+ [README](https://github.com/facebookresearch/dinov2/blob/main/docs/README_CELL_DINO.md)
+
+
+ ## Licenses
+
+ Code is released under the CC BY NC License. See [LICENSE_CELLDINO](LICENSE_CELLDINO) for additional details.
+ Model weights are released under the FAIR Noncommercial Research License. See [LICENSE_CELLDINO_WEIGHTS](LICENSE_CELLDINO_WEIGHTS) for additional details.
+

dinov2/configs/eval/celldino.yaml

@@ -0,0 +1,14 @@
+student:
+  arch: vit_large
+  patch_size: 16
+  num_register_tokens: 0
+  interpolate_antialias: false
+  interpolate_offset: 0.1
+  drop_path_rate: 0.1
+  in_chans: 4
+  block_chunks: 4
+teacher:
+  in_chans: 4  
+crops:
+  global_crops_size: 224
+  local_crops_size: 96

dinov2/configs/eval/channeldino_ext_chammi.yaml

@@ -0,0 +1,35 @@
+train: 
+  batch_size_per_gpu: 32
+  OFFICIAL_EPOCH_LENGTH: 450
+  cell_augmentation: true
+  channel_adaptive: true
+student:
+  arch: vit_large
+  patch_size: 16
+  num_register_tokens: 0
+  interpolate_antialias: false
+  interpolate_offset: 0.1
+  drop_path_rate: 0.1
+  in_chans: 1
+  block_chunks: 4
+  channel_adaptive: true
+teacher:
+  momentum_teacher: 0.996
+  warmup_teacher_temp_epochs: 20
+  in_chans: 1
+  channel_adaptive: true
+crops:
+  global_crops_scale:
+  - 0.4
+  - 1.0
+  local_crops_number: 8
+  local_crops_scale:
+  - 0.005
+  - 0.4
+  global_crops_size: 224
+  local_crops_size: 96
+optim:
+  weight_decay_end: 0.2
+  base_lr: 5.0e-4 
+  warmup_epochs: 20
+  epochs: 400

dinov2/configs/ssl_default_config.yaml

@@ -68,6 +68,7 @@ train:
   OFFICIAL_EPOCH_LENGTH: 1250
   cache_dataset: true
   centering: "centering" # or "sinkhorn_knopp"
+  cell_augmentation: false
 student:
   arch: vit_large
   patch_size: 16
@@ -83,12 +84,16 @@ student:
   num_register_tokens: 0
   interpolate_antialias: false
   interpolate_offset: 0.1
+  in_chans: 3
+  channel_adaptive: false
 teacher:
   momentum_teacher: 0.992
   final_momentum_teacher: 1
   warmup_teacher_temp: 0.04
   teacher_temp: 0.07
   warmup_teacher_temp_epochs: 30
+  in_chans: 3
+  channel_adaptive: false
 optim:
   epochs: 100
   weight_decay: 0.04

dinov2/configs/train/hpafov_vitl16.yaml

@@ -0,0 +1,32 @@
+train: 
+  batch_size_per_gpu: 16
+  OFFICIAL_EPOCH_LENGTH: 450
+  cell_augmentation: true
+student: 
+  arch: vit_large
+  patch_size: 16
+  in_chans: 4
+  drop_path_rate: 0.1
+  block_chunks: 4
+teacher:
+  momentum_teacher: 0.996
+  warmup_teacher_temp_epochs: 20
+  in_chans: 4
+optim:
+  weight_decay_end: 0.2
+  base_lr: 5.0e-4 
+  warmup_epochs: 20   
+crops:
+  global_crops_scale:
+  - 0.4
+  - 1.0
+  local_crops_number: 8
+  local_crops_scale:
+  - 0.005
+  - 0.4
+  global_crops_size: 224
+  local_crops_size: 96
+evaluation:  
+  eval_period_iterations: 4500
+
+

dinov2/configs/train/hpafov_vitl16_boc.yaml

@@ -0,0 +1,31 @@
+train: 
+  batch_size_per_gpu: 16
+  OFFICIAL_EPOCH_LENGTH: 450
+  cell_augmentation: true
+  channel_adaptive: true
+student: 
+  arch: vit_large
+  patch_size: 16
+  in_chans: 1
+  drop_path_rate: 0.1
+  block_chunks: 4
+teacher:
+  momentum_teacher: 0.996
+  warmup_teacher_temp_epochs: 20
+  in_chans: 1
+crops:
+  global_crops_scale:
+  - 0.4
+  - 1.0
+  local_crops_number: 8
+  local_crops_scale:
+  - 0.005
+  - 0.4
+  global_crops_size: 224
+  local_crops_size: 96
+optim:
+  weight_decay_end: 0.2
+  base_lr: 5.0e-4 
+  warmup_epochs: 20
+  epochs: 400  
+

dinov2/configs/train/hpaone_vitl16.yaml

@@ -0,0 +1,30 @@
+train: 
+  batch_size_per_gpu: 32
+  OFFICIAL_EPOCH_LENGTH: 1756
+  cell_augmentation: true
+student: 
+  arch: vit_large
+  patch_size: 16
+  in_chans: 4
+  drop_path_rate: 0.1
+  block_chunks: 4
+teacher:
+  momentum_teacher: 0.996
+  warmup_teacher_temp_epochs: 20
+  in_chans: 4
+optim:
+  weight_decay_end: 0.2
+  base_lr: 5.0e-4 
+  warmup_epochs: 20
+crops:
+  global_crops_scale:
+  - 0.4
+  - 1.0
+  local_crops_number: 8
+  local_crops_scale:
+  - 0.005
+  - 0.4
+  global_crops_size: 224
+  local_crops_size: 96
+evaluation:  
+  eval_period_iterations: 4500

dinov2/data/__init__.py

@@ -8,3 +8,5 @@
 from .collate import collate_data_and_cast
 from .masking import MaskingGenerator
 from .augmentations import DataAugmentationDINO
+from .cell_augmentations import CellAugmentationDINO
+from .accumulators import NoOpAccumulator, ResultsAccumulator