| | --- |
| | license: mit |
| | pipeline_tag: zero-shot-image-classification |
| | --- |
| | |
| | The model that corresponds to Q-Align (ICML2024). |
| |
|
| | ## Quick Start with AutoModel |
| |
|
| | For this image,  start an AutoModel scorer with `transformers==4.36.1`: |
| |
|
| | ```python |
| | import requests |
| | import torch |
| | from transformers import AutoModelForCausalLM |
| | |
| | model = AutoModelForCausalLM.from_pretrained("q-future/one-align", trust_remote_code=True, attn_implementation="eager", |
| | torch_dtype=torch.float16, device_map="auto") |
| | |
| | from PIL import Image |
| | url = "https://raw.githubusercontent.com/Q-Future/Q-Align/main/fig/singapore_flyer.jpg" |
| | image = Image.open(requests.get(url,stream=True).raw) |
| | model.score([image], task_="quality", input_="image") |
| | # task_ : quality | aesthetics; # input_: image | video |
| | ``` |
| |
|
| |
|
| | Result should be 1.911 (in range [1,5], higher is better). |
| |
|
| | From paper: `arxiv.org/abs/2312.17090`. |
| | |
| | ## Syllabus |
| |
|
| |  |
| |
|
| | ## IQA Results (Spearman/Pearson/Kendall) |
| | |Datasets | KonIQ (NR-IQA, seen) | SPAQ (NR-IQA, Seen) | KADID (FR-IQA, Seen) | LIVE-C (NR-IQA, *Unseen*) | LIVE (FR-IQA, *Unseen*) | CSIQ (FR-IQA, *Unseen*) | AGIQA (AIGC, *Unseen*)| |
| | | --- | --- | --- | --- | --- | --- | --- | --- | |
| | | *Previous SOTA* | 0.916/0.928 (MUSIQ, ICCV2021) | 0.922/0.919 (LIQE, CVPR2023) | 0.934/0.937 (CONTRIQUE, TIP2022) | NA | NA | NA | NA | |
| | | Q-Align (IQA) | 0.937/0.945/0.785 | 0.931/0.933/0.763 | 0.934/0.934/0.777 | 0.887/0.896/0.706 | 0.874/0.840/0.682 | 0.845/0.876/0.654 | 0.731/0.791/0.529 | |
| | | Q-Align (IQA+VQA) | **0.944**/0.949/**0.797** | 0.931/0.934/0.764 | **0.952**/**0.953**/**0.809** | **0.892**/**0.899**/**0.715** | 0.874/0.846/0.684 | 0.852/0.876/0.663 | 0.739/0.782/0.526 | |
| | | **OneAlign** (IQA+IAA+VQA) | 0.941/**0.950**/0.791 | **0.932**/**0.935**/**0.766** | 0.941/0.942/0.791 | 0.881/0.894/0.699 | **0.887**/**0.856**/**0.699** | **0.881**/**0.906**/**0.699** | **0.801**/**0.838**/**0.602** | |
| |
|
| | ## IAA Results (Spearman/Pearson) |
| | | Dataset | AVA_test | |
| | | --- | --- | |
| | | *VILA (CVPR, 2023)* | 0.774/0.774 | |
| | | *LIQE (CVPR, 2023)* | 0.776/0.763 | |
| | | *Aesthetic Predictor (retrained on AVA_train)* | 0.721/0.723 | |
| | | Q-Align (IAA) | 0.822/0.817 | |
| | | **OneAlign** (IQA+IAA+VQA) | **0.823**/**0.819** | |
| |
|
| | ## VQA Results (Spearman/Pearson) |
| |
|
| | | Datasets | LSVQ_test | LSVQ_1080p | KoNViD-1k | MaxWell_test | |
| | | --- | --- | --- | --- | --- | |
| | | *SimpleVQA (ACMMM, 2022)* | 0.867/0.861 | 0.764/0.803 | 0.840/0.834 | 0.720/0.715 | |
| | | *FAST-VQA (ECCV 2022)* | 0.876/0.877 | 0.779/0.814 | 0.859/0.855 | 0.721/0.724 | |
| | | Q-Align (VQA) | 0.883/0.882 | 0.797/0.830 | 0.865/0.877 | 0.780/0.782 | |
| | | Q-Align (IQA+VQA) | 0.885/0.883 | 0.802/0.829 | 0.867/0.880 | **0.781**/**0.787** | |
| | | **OneAlign** (IQA+IAA+VQA) | **0.886**/**0.886** | **0.803**/**0.837** | **0.876**/**0.888** | **0.781**/0.786| |
| | |
