ODinW
Object Detection in the Wild (ODinW) benchmark for evaluating object detection models' task-level transfer ability across diverse real-world datasets in terms of prediction accuracy and adaptation efficiency
Qwen3.6 Plus from Alibaba Cloud / Qwen Team currently leads the ODinW leaderboard with a score of 0.518 across 15 evaluated AI models.
What ODinW measures
ODinW is a image benchmark that evaluates large language models on vision tasks. LLM Stats tracks 15 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.4, with the leader reaching 0.5.
Compare leaders on the best AI for vision leaderboards.
Publication
- Paper
- Grounded Language-Image Pre-training
- Authors
- Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, and 8 others
- Published
- arXiv
- 2112.03857
Abstract
This paper presents a grounded language-image pre-training (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations. GLIP unifies object detection and phrase grounding for pre-training. The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned representation semantic-rich. In our experiments, we pre-train GLIP on 27M grounding data, including 3M human-annotated and 24M web-crawled image-text pairs. The learned representations demonstrate strong zero-shot and few-shot transferability to various object-level recognition tasks. 1) When directly evaluated on COCO and LVIS (without seeing any images in COCO during pre-training), GLIP achieves 49.8 AP and 26.9 AP, respectively, surpassing many supervised baselines. 2) After fine-tuned on COCO, GLIP achieves 60.8 AP on val and 61.5 AP on test-dev, surpassing prior SoTA. 3) When transferred to 13 downstream object detection tasks, a 1-shot GLIP rivals with a fully-supervised Dynamic Head. Code is released at https://github.com/microsoft/GLIP.
Qwen3.6 Plus leads with 51.8%, followed by Qwen3.6-35B-A3B at 50.8% and Qwen3 VL 235B A22B Instruct at 48.6%.
Progress Over Time
Interactive timeline showing model performance evolution on ODinW
ODinW Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Alibaba Cloud / Qwen Team | — | 1.0M | $0.50 / $3.00 | ||
| 2 | Alibaba Cloud / Qwen Team | 35B | — | — | ||
| 3 | Alibaba Cloud / Qwen Team | 236B | 262K | $0.30 / $1.49 | ||
| 4 | Alibaba Cloud / Qwen Team | 4B | 262K | $0.10 / $0.60 | ||
| 5 | Alibaba Cloud / Qwen Team | 31B | — | — | ||
| 6 | Alibaba Cloud / Qwen Team | 33B | — | — | ||
| 7 | Alibaba Cloud / Qwen Team | 9B | 262K | $0.08 / $0.50 | ||
| 8 | Alibaba Cloud / Qwen Team | 122B | 262K | $0.40 / $3.20 | ||
| 9 | Alibaba Cloud / Qwen Team | 236B | — | — | ||
| 10 | Alibaba Cloud / Qwen Team | 35B | 262K | $0.25 / $2.00 | ||
| 11 | Alibaba Cloud / Qwen Team | 7B | — | — | ||
| 12 | Alibaba Cloud / Qwen Team | 31B | — | — | ||
| 13 | Alibaba Cloud / Qwen Team | 27B | 262K | $0.30 / $2.40 | ||
| 14 | Alibaba Cloud / Qwen Team | 9B | 262K | $0.18 / $2.09 | ||
| 15 | Alibaba Cloud / Qwen Team | 4B | 262K | $0.10 / $1.00 |
FAQ
Common questions about ODinW.
More evaluations to explore
Related benchmarks in the same category
Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions
MMMU (Massive Multi-discipline Multimodal Understanding) is a benchmark designed to evaluate multimodal models on college-level subject knowledge and deliberate reasoning. Contains 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering across 30 subjects and 183 subfields.
A more robust multi-discipline multimodal understanding benchmark that enhances MMMU through a three-step process: filtering text-only answerable questions, augmenting candidate options, and introducing vision-only input settings. Achieves significantly lower model performance (16.8-26.9%) compared to original MMMU, providing more rigorous evaluation that closely mimics real-world scenarios.
CharXiv-R is the reasoning component of the CharXiv benchmark, focusing on complex reasoning questions that require synthesizing information across visual chart elements. It evaluates multimodal large language models on their ability to understand and reason about scientific charts from arXiv papers through various reasoning tasks.
MathVista evaluates mathematical reasoning of foundation models in visual contexts. It consists of 6,141 examples derived from 28 existing multimodal datasets and 3 newly created datasets (IQTest, FunctionQA, and PaperQA), combining challenges from diverse mathematical and visual tasks to assess models' ability to understand complex figures and perform rigorous reasoning.
AI2D is a dataset of 4,903 illustrative diagrams from grade school natural sciences (such as food webs, human physiology, and life cycles) with over 15,000 multiple choice questions and answers. The benchmark evaluates diagram understanding and visual reasoning capabilities, requiring models to interpret diagrammatic elements, relationships, and structure to answer questions about scientific concepts represented in visual form.