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cfcamo
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Model Details
Model Provider
cfcamo
Model Tree
Qwen/Qwen3-VL-4B-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoModelForImageTextToText, AutoProcessorfrom PIL import Imageckpt = "cfcamo/cfcamo-rl-full" # or a local pathprocessor = AutoProcessor.from_pretrained(ckpt)model = AutoModelForImageTextToText.from_pretrained(ckpt, torch_dtype="auto", device_map="auto",).eval()SYS = ("You are a camouflaged object detector. Output in this exact format:\n\n""<think>your reasoning here</think>\n""followed by ONE of:\n"" - <bbox>[x1,y1,x2,y2]</bbox> for a single camouflaged object\n"" - <bbox>[[x1,y1,x2,y2],[x3,y3,x4,y4]]</bbox> for multiple objects\n"" - <no_camouflage/> if no camouflaged object is present\n\n""Coordinates are normalized to [0, 1000] where 1000 = full image dimension.")USR = ("Identify and locate any camouflaged object in the image.\n\n""In <think></think>, briefly consider scene textures, visual anomalies, ""and if any object blends in. Then output ONE of:\n""- <bbox>[x1,y1,x2,y2]</bbox> for one object, or [[x1,y1,x2,y2],...] for multiple\n""- <no_camouflage/> if no camouflaged object")image = Image.open("path/to/image.jpg").convert("RGB")messages = [{"role": "system", "content": SYS},{"role": "user", "content": [{"type": "image", "image": image},{"type": "text", "text": USR},]},]inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True,return_dict=True, return_tensors="pt",).to(model.device)out = model.generate(**inputs, max_new_tokens=512, do_sample=False)print(processor.batch_decode(out[:, inputs["input_ids"].shape[1]:],skip_special_tokens=True)[0])
Output is one of:
markdown
<think>...</think><bbox>[x1,y1,x2,y2]</bbox> # single box<think>...</think><bbox>[[...],[...]]</bbox> # multi-box<think>...</think><no_camouflage/> # abstain
Box coordinates are in [0, 1000] normalized image space.
bash
git clone https://github.com/suhang2000/CFCamo && cd CFCamopip install -e ".[eval]"huggingface-cli download --repo-type dataset cfcamo/CF-COD --local-dir data/cfcod# (place upstream COD into data/cfcod/<source>/{Imgs,GT}/ — see dataset card)huggingface-cli download cfcamo/cfcamo-rl-full --local-dir checkpoints/cfcamo-rl-fullpython scripts/eval/eval_cfcod.py \--cf-manifest data/cfcod/test/cf_manifest_test.jsonl \--data-root data/cfcod \--models "CFCamo=checkpoints/cfcamo-rl-full" \--out-dir results/cfcod_eval
bibtex
@article{li2026cfcamo,title = {{CFCamo}: A Counterfactual Detect-or-Abstain Framework for Camouflaged Object Detection},author = {Li, Suhang and Yoshie, Osamu and Ieiri, Yuya},journal = {arXiv preprint arXiv:2606.11231},year = {2026}}