| --- |
| license: apache-2.0 |
| base_model: |
| - openai/clip-vit-base-patch16 |
| - openai/clip-vit-base-patch32 |
| pipeline_tag: zero-shot-image-classification |
| tags: |
| - clip |
| - vision-language |
| - prompt-learning |
| - contrastive-learning |
| - distribution-shift |
| --- |
| |
| # ReCalCon |
|
|
| Checkpoints for **Re-calibrated Contrastive Loss for Transformation-Aware Prompt Conditioning in Vision-Language |
| Models** (BMVC 2026). [Paper](https://arxiv.org/abs/2609.06967) · [Code](https://github.com/SoongE/ReCalCon) |
|
|
| Each folder holds `model.safetensors` (the full model state dict, fp32) and `config.json`, which names the released |
| config to evaluate it with. |
|
|
| ## Usage |
|
|
| ```bash |
| hf download SoongE/ReCalCon --include "distribution_shift/imagenet_b16/*" --local-dir checkpoints |
| python -m scripts.eval --config imagenet_b16 \ |
| --eval.checkpoint checkpoints/distribution_shift/imagenet_b16/model.safetensors |
| ``` |
|
|
| ## Checkpoints |
|
|
| **Distribution shift** (top-1 accuracy, %) |
|
|
| | Checkpoint | Row | ImageNet | -R | -A | -V2 | -Sketch | ObjectNet | |
| | --- | --- | --- | --- | --- | --- | --- | --- | |
| | `distribution_shift/imagenet_b16` | ViT-B/16, Update, ImageNet | 83.284 | 71.607 | 54.893 | 74.1 | 51.781 | 58.388 | |
| | `distribution_shift/imagenet_b32` | ViT-B/32, Update, ImageNet | 79.354 | 62.41 | 32.573 | 68.15 | 44.503 | 49.203 | |
| | `distribution_shift/imagenet_petl_b16` | ViT-B/16, Freeze (PETL), ImageNet | 80.206 | 73.163 | 50.867 | 70.13 | 49.614 | 55.691 | |
| | `distribution_shift/imagenet_petl_b32` | ViT-B/32, Freeze (PETL), ImageNet | 76.12 | 63.563 | 30.453 | 64.52 | 42.267 | 46.662 | |
|
|
| iWildCam (macro-F1, %). The released evaluation reports the OOD split. |
|
|
| | Checkpoint | Row | ID (macro-F1) | OOD (macro-F1) | |
| | --- | --- | --- | --- | |
| | `distribution_shift/iwildcam_b16` | ViT-B/16, Update, iWildCam | 51.253 | 37.803 | |
| | `distribution_shift/iwildcam_b32` | ViT-B/32, Update, iWildCam | 42.116 | 29.275 | |
| | `distribution_shift/iwildcam_petl_b16` | ViT-B/16, Freeze (PETL), iWildCam | 45.49 | 30.327 | |
| | `distribution_shift/iwildcam_petl_b32` | ViT-B/32, Freeze (PETL), iWildCam | 34.709 | 24.502 | |
|
|
| **Transfer learning** (top-1 accuracy, %) |
|
|
| | Checkpoint | Dataset | Accuracy | |
| | --- | --- | --- | |
| | `transfer/caltech101` | Caltech101 | 97.85 | |
| | `transfer/flowers102` | Flowers102 | 98.894 | |
| | `transfer/pcam` | PCam | 89.413 | |
| | `transfer/stanfordcars` | StanfordCars | 91.419 | |
|
|
| ## License |
|
|
| Apache License 2.0. The models are fine-tuned from OpenAI CLIP (MIT License); each evaluation dataset keeps its own |
| terms. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{oh2026recalibrated, |
| title = {Re-calibrated Contrastive Loss for Transformation-Aware Prompt Conditioning in Vision-Language Models}, |
| author = {Oh, Seungmin and Kang, Seunghun and Ryu, Jongbin}, |
| booktitle = {Proceedings of the British Machine Vision Conference}, |
| year = {2026}, |
| publisher = {BMVA}, |
| url = {https://arxiv.org/abs/2609.06967} |
| } |
| ``` |
|
|
