EdgeCVS-5M (fold 0)

README.md

@@ -1,3 +1,65 @@
 ---
 license: cc-by-nc-4.0
+library_name: pytorch
+tags:
+  - surgical-video
+  - laparoscopic-cholecystectomy
+  - critical-view-of-safety
+  - image-classification
+  - semantic-segmentation
+  - knowledge-distillation
 ---
+
+# EdgeCVS
+
+Models from [**EdgeCVS: Democratization of Surgical AI with a Distilled Edge-Deployable
+Critical View of Safety (CVS) Model**](https://papers.miccai.org/miccai-2026/paper/1745_paper.pdf) (MICCAI 2026).
+
+EdgeCVS assesses the three Critical View of Safety criteria in laparoscopic cholecystectomy
+from single frames, with a small model distilled from large multi-task teachers that runs in
+real time on a CPU.
+
+Code: [github.com/IMSY-DKFZ/edgecvs](https://github.com/IMSY-DKFZ/edgecvs)
+
+## Models
+
+Each model lives in its own folder, named like the code's `exp` option:
+
+| Model (`exp`) | Backbone | Params | Weights | SAGES-CVS 2024 test mAP |
+|---|---|---|---|---|
+| [`edgecvs-5m`](edgecvs-5m/) | EdgeNeXt-Small | 5.6M | fp16, 11 MB | 66.4 |
+
+```
+edgecvs-5m/
+├── config.yaml                     model configuration used to build the network
+└── edgecvs-5m-fold0.safetensors    weights (fold 0, fp16; loaded into an fp32 model)
+```
+
+## EdgeCVS-5M
+
+- **Input:** RGB frame, resized to 448×448, ImageNet normalization
+- **Output:** probabilities for C1 (two structures), C2 (hepatocystic triangle) and
+  C3 (cystic plate); optional 7-class anatomy segmentation (background, cystic plate,
+  hepatocystic triangle, cystic artery, cystic duct, gallbladder, tool)
+- **Deployment:** the segmentation head is optional; the classifier alone is used for the CSV
+  predictions and the speed figures below
+
+
+## License
+
+CC BY-NC 4.0: the models are trained on data released for non-commercial use.
+
+## Citation
+
+```bibtex
+@InProceedings{YamAmi_EdgeCVS_MICCAI2026,
+        author = { Yamlahi, Amine AND Hennighausen, Jakob AND Hansen, Pascal AND Leeb, David AND Maier-Hein, Lena},
+        title = { { EdgeCVS: Democratization of surgical AI with a Distilled Edge-Deployable Critical View of Safety (CVS) model } },
+        booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
+        year = {2026},
+        publisher = {Springer Nature Switzerland},
+        volume = {LNCS 16892},
+        month = {September},
+        page = {pending}
+}
+```

edgecvs-5m/config.yaml

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+exp: edgecvs-5m
+vmodel: SegFormerAuxSeg
+strategy: aux_seg_disk
+aux_seg: true
+model_name: edgenext_small.usi_in1k
+height: 448
+width: 448
+target_size: 3
+col0: C1
+n_fold: 5
+trn_fold:
+- 0
+seed: 1
+nworkers: 4
+aspp:
+  pretrained: false
+  encoder_out_indices:
+  - 0
+  - 1
+  - 2
+  - 3
+  bottleneck_out_channels: 256
+  aspp_input_channels: 256
+  aspp_out_channels: 128
+  aspp_dilations:
+  - 1
+  - 3
+  - 6
+  - 9
+  decoderblock: Upsample
+  decoder_hidden_dim: 64
+  scale: 4
+seg:
+  seg_input_size:
+    height: 448
+    width: 448
+  classes:
+  - background
+  - cystic_plate
+  - calot_triangle
+  - cystic_artery
+  - cystic_duct
+  - gallbladder
+  - tool
+  aux_weight: 0.1
+valid:
+  tta: false
+  transforms:
+  - name: Resize
+    params:
+      height: 448
+      width: 448
+      p: 1.0
+  - name: Normalize
+    params:
+      mean:
+      - 0.485
+      - 0.456
+      - 0.406
+      std:
+      - 0.229
+      - 0.224
+      - 0.225
+  - name: ToTensorV2
+    params: {}

edgecvs-5m/edgecvs-5m-fold0.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0af2a5f7f333afb86ae1f03f507d36cc334e8aac96886418b99950ef0a0a8174
+size 11271366