metadata
license: apache-2.0
base_model: Qwen/Qwen2.5-VL-7B-Instruct
pipeline_tag: video-text-to-text
library_name: transformers
tags:
- temporal-forgery-localization
- video-forensics
- grpo
ForgeLoc-R1 checkpoints
Qwen2.5-VL-7B-Instruct fine-tuned with GRPO to localize the manipulated segments of a video. Every folder holds a full checkpoint in Hugging Face format.
| Folder | Model | Training data | Test mIoU (%) |
|---|---|---|---|
af |
ForgeLoc-R1 | ActivityForensics | 64.27 |
ddl |
ForgeLoc-R1 | DDL | 53.03 |
baselines/af_grpo |
GRPO | ActivityForensics | 51.28 |
baselines/af_video-r1 |
Video-R1 | ActivityForensics | 54.29 |
baselines/af_tempsamp-r1 |
TempSamp-R1 | ActivityForensics | 59.93 |
baselines/ddl_grpo |
GRPO | DDL | 31.89 |
baselines/ddl_video-r1 |
Video-R1 | DDL | 33.25 |
baselines/ddl_tempsamp-r1 |
TempSamp-R1 | DDL | 50.92 |
ablations/af_noanchor |
without anchor injection | ActivityForensics | 58.61 |
ablations/af_noprune |
without advantage pruning | ActivityForensics | 62.05 |
ablations/af_noos |
without count balancing (m=1) | ActivityForensics | 59.53 |
ablations/af_notransform |
without the asymmetric advantage transform | ActivityForensics | 62.90 |
ablations/af_bestoverlap |
set term replaced by the best-overlap soft F1 | ActivityForensics | 65.35 |
ablations/af_g16 |
G'=16 | ActivityForensics | 63.53 |
ablations/af_eps0.01 |
Sinkhorn coefficient 0.01 | ActivityForensics | 63.49 |
ablations/af_seed7 |
ForgeLoc-R1, seed 7 | ActivityForensics | 63.27 |
ablations/ddl_noos |
without count balancing (m=1) | DDL | 50.17 |
mIoU is measured on the test splits: 1,565 ActivityForensics videos and 1,500 DDL videos.
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"sdzt/ForgeLoc-R1", subfolder="af", torch_dtype="bfloat16", device_map="auto"
)
processor = AutoProcessor.from_pretrained("sdzt/ForgeLoc-R1", subfolder="af")