| --- |
| library_name: open_clip |
| tags: |
| - clip |
| - cod10k |
| - camouflage-object-detection |
| - segmentation |
| - gmpo |
| --- |
| |
| # 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. |
|
|
