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COD10K_CAM_5Way_Segmentation_Checkpoints
KRMayD/COD10K_CAM_5Way_Segmentation_Checkpoints
COD10K_CAM_5Way_Segmentation_Checkpoints is a machine learning model from KRMayD. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for open_clip.
This repository contains the five OpenCLIP ViT-B-32-quickgelu checkpoints used in one controlled COD10K CAM segmentation comparison.
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Updated Jul 26, 2026
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At a glance
- Library
- open_clip
- Access
- Public
- Created
- Jul 26, 2026
- Updated
- Jul 26, 2026
- SHA
- e51cba06
Model card
From the Hugging Face model README
COD10K CAM Five-Way Segmentation Checkpoints
This repository contains the five OpenCLIP ViT-B-32-quickgelu checkpoints
used in one controlled COD10K CAM segmentation comparison.
Checkpoints
| File | Training method |
|---|---|
baseline_openai_clip_vit_b32_quickgelu.pt | Unmodified OpenAI CLIP baseline |
clip_posttrained_explicitneg_2886_seed0_final_model.pt | Native CLIP post-training on positive image-caption pairs |
cliprefine_posttrained_explicitneg_2886_seed0_final_model.pt | Native CLIP-Refine post-training on positive image-caption pairs |
gmpo_global_ind_explicitneg_2886_seed0_epoch_3.pt | GMPO with one frozen reference-derived IND four-way vector for all samples |
gmpo_sample_ind_explicitneg_2886_seed0_epoch_3.pt | GMPO with a frozen reference-derived IND four-way vector per sample |
All checkpoints are plain PyTorch/OpenCLIP state dictionaries compatible with
the ViT-B-32-quickgelu architecture.
Shared Data and Evaluation Protocol
- Train split: 2,886 COD10K CAM pairs after removing 154 SHA-256-verified MM-CamObj Easy/Hard benchmark overlaps.
- CSV inputs:
filename,filename_neg,Caption,Caption_neg. - Negative image: Stable-Diffusion inpainted target-mask region composited on the original background.
- Caption policy: image-specific base background caption plus explicit animal
presence (
T+) or absence (T-) sentence. - Test split: 2,026 COD10K CAM images.
- Prompt:
There is a/an {animal} visually blended into its surroundings. - Saliency parameters:
vbeta=1.0,vvar=0.3,vlayer=8, seed 42. - Metrics: mean DSC and NSD with 2-pixel tolerance.
The exact paths, settings, data checksum, and metrics are included in
comparison_protocol.json and metrics_summary.json.
Segmentation Results
| Model | DSC | NSD |
|---|---|---|
| Baseline CLIP | 0.352689 | 0.375161 |
| CLIP post-training | 0.359849 | 0.383442 |
| CLIP-Refine post-training | 0.359409 | 0.383454 |
| GMPO Global-IND | 0.378582 | 0.402299 |
| GMPO Sample-IND | 0.356773 | 0.379253 |
Important Comparison Detail
The train split, starting checkpoint, number of epochs, seed, test images,
prompt, saliency settings, and metric code are fixed across the comparison.
GMPO necessarily uses the full four-tuple. CLIP and CLIP-Refine use only the
positive image-caption pair by their native definitions and retain their
native optimizer settings; see comparison_protocol.json for the exact
values.
- Library
- open_clip
- Created
- Jul 26, 2026
- Updated
- Jul 26, 2026