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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 Image ckpt = "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:
text
<think>...</think><bbox>[x1,y1,x2,y2]</bbox> # single box<think>...</think><bbox>[[...],[...]]</bbox> # multi-box<think>...</think><no_camouflage/> # abstainBox 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-full python 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_evalbibtex
@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}}from transformers import AutoModelForImageTextToText, AutoProcessorfrom PIL import Image ckpt = "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])<think>...</think><bbox>[x1,y1,x2,y2]</bbox> # single box<think>...</think><bbox>[[...],[...]]</bbox> # multi-box<think>...</think><no_camouflage/> # abstaingit 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-full python 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@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}}