Abk802 commited on
Commit
3560199
·
verified ·
1 Parent(s): 913c6d8

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +7 -7
README.md CHANGED
@@ -16,9 +16,9 @@ tags:
16
  - benchmark
17
  configs:
18
  - config_name: OPTICS-CNT
19
- data_dir: OPTICS-CNT/images
20
  - config_name: OPTICS-CMP
21
- data_dir: OPTICS-CMP/images
22
  ---
23
  <h1 align="center">OPTICS: Commonsense Reasoning over Visual Object Properties</h1>
24
 
@@ -26,8 +26,8 @@ configs:
26
 
27
  Inspired by human object categorization, OPTICS (formerly ORBIT) is a benchmark suite for evaluating how Vision–Language Models (VLMs) perceive and reason about object properties. It spans four object property dimensions (physical, taxonomic, functional, relational), three levels of reasoning complexity (direct recognition, property inference, counterfactual reasoning), and three image types (photographic/real, animated, AI-generated). It has two parts: **OPTICS-CNT** (exact counting) and **OPTICS-CMP** (count comparison).
28
 
29
- <!-- 📄 [Paper](https://arxiv.org/pdf/2508.10956) |
30
- 📚 [arXiv](https://arxiv.org/abs/2508.10956) | -->
31
  💻 [Code](https://github.com/AbhishekKolari/OPTICS)
32
 
33
  </div>
@@ -47,11 +47,11 @@ cmp = load_dataset("Abk802/OPTICS", "OPTICS-CMP", split="train")
47
 
48
  ```
49
  OPTICS-CNT/
50
- ├── images/ # images + metadata.csv per image type
51
  └── <benchmark>.json
52
  OPTICS-CMP/
53
- ├── images/ # merged image pairs + metadata.csv
54
- └── <benchmark>.json
55
  ```
56
 
57
  ### OPTICS-CNT
 
16
  - benchmark
17
  configs:
18
  - config_name: OPTICS-CNT
19
+ data_dir: OPTICS-CNT/data
20
  - config_name: OPTICS-CMP
21
+ data_dir: OPTICS-CMP/merged_images
22
  ---
23
  <h1 align="center">OPTICS: Commonsense Reasoning over Visual Object Properties</h1>
24
 
 
26
 
27
  Inspired by human object categorization, OPTICS (formerly ORBIT) is a benchmark suite for evaluating how Vision–Language Models (VLMs) perceive and reason about object properties. It spans four object property dimensions (physical, taxonomic, functional, relational), three levels of reasoning complexity (direct recognition, property inference, counterfactual reasoning), and three image types (photographic/real, animated, AI-generated). It has two parts: **OPTICS-CNT** (exact counting) and **OPTICS-CMP** (count comparison).
28
 
29
+ 📄 [Paper](https://arxiv.org/pdf/2508.10956) |
30
+ 📚 [arXiv](https://arxiv.org/abs/2508.10956) |
31
  💻 [Code](https://github.com/AbhishekKolari/OPTICS)
32
 
33
  </div>
 
47
 
48
  ```
49
  OPTICS-CNT/
50
+ ├── data/ # images + metadata.csv per image type
51
  └── <benchmark>.json
52
  OPTICS-CMP/
53
+ ├── merged_images/ # merged image pairs + metadata.csv
54
+ └── <merged_benchmark>.json
55
  ```
56
 
57
  ### OPTICS-CNT