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Coobiw / IP-IQA

[ICME2024, Official Code] for paper "Bringing Textual Prompt to AI-Generated Image Quality Assessment"
https://arxiv.org/abs/2403.18714
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The results are different from the article #2

Open ctxya1207 opened 4 months ago

ctxya1207 commented 4 months ago

Why did the srocc of Aling I ran only reach more than 0.63?

ctxya1207 commented 4 months ago

{ "model": { "arch": "ipiqa", "base_ckpt": "./cache/ckpt/clip/openai/resnet/RN50.pt", "input_resolution": 512, "output_dim": 2, "load_finetuned": false, "head_scale": 10.0, "use_mlp_head": true, "dropout_rate": 0.5, "freeze_text": true, "qa_token": true }, "dataset": { "data_path": "./cache/data/aigc_qa_3k/mos_joint.xlsx", "vis_root": "./cache/data/aigc_qa_3k/AGIQA-3K/", "transform_train": { "name": "image_train_processor", "cfg": { "image_size": 512 } }, "transform_val": { "name": "image_eval_processor", "cfg": { "image_size": 512 } } }, "run": { "task": "agiqa_doublescore", "resume_ckpt_path": null, "lr_sched": "constant_lr", "lr_decay_rate": null, "warmup_lr": -1, "warmup_steps": 0, "min_lr": 1e-05, "init_lr": 1e-05, "lr_layer_decay": 1, "weight_decay": 0, "beta2": 0.999, "batch_size": 16, "batch_size_val": 16, "num_worker": 2, "max_epoch": 100, "log_freq": 50, "accum_grad_iters": 1, "grad_norm_clip": null, "output_dir": "output/agiqa3k/ipiqa", "evaluate": false, "eval_freq": 1, "save_freq": 100, "amp": false, "device": "cuda", "distributed": false, "dist_url": "env://" } } {"train_lr": "0.000010", "train_loss": "0.780194"} {"val_agg_metrics": 4.064380826685456, "val_qual_agg": 2.239623748382492, "val_qual_PLCC": 0.8495989331774007, "val_qual_SROCC": 0.790777095277724, "val_qual_KROCC": 0.599247719927367, "val_align_agg": 1.8247570783029643, "val_align_PLCC": 0.7595706495304722, "val_align_SROCC": 0.6206569980669238, "val_align_KROCC": 0.44452943070556855, "val_best_epoch": 0} {"train_lr": "0.000010", "train_loss": "0.357433"} {"val_agg_metrics": 4.20305048717456, "val_qual_agg": 2.3316847769945483, "val_qual_PLCC": 0.8749314050815291, "val_qual_SROCC": 0.8248382229404648, "val_qual_KROCC": 0.6319151489725546, "val_align_agg": 1.8713657101800123, "val_align_PLCC": 0.7882834053250045, "val_align_SROCC": 0.6287356671419521, "val_align_KROCC": 0.45434663771305556, "val_best_epoch": 1} {"train_lr": "0.000010", "train_loss": "0.303964"} {"val_agg_metrics": 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"val_qual_KROCC": 0.6544843503817428, "val_align_agg": 1.9956006274629243, "val_align_PLCC": 0.8169585603404932, "val_align_SROCC": 0.6773797505603084, "val_align_KROCC": 0.501262316562123, "val_best_epoch": 97} {"train_lr": "0.000010", "train_loss": "0.024417"} {"val_agg_metrics": 4.404147390995561, "val_qual_agg": 2.3962038303515425, "val_qual_PLCC": 0.8944198198003533, "val_qual_SROCC": 0.8434088063141504, "val_qual_KROCC": 0.6583752042370389, "val_align_agg": 2.0079435606440184, "val_align_PLCC": 0.8218869397979285, "val_align_SROCC": 0.6810495964563689, "val_align_KROCC": 0.505007024389721, "val_best_epoch": 97} this is my log

Coobiw commented 4 months ago

The best result in the paper is equipped with the Image2Prompt Continue Pretraining. on DiffusionDB Subset. This weight may not open-source in the future.

---- Replied Message ---- From ctxya1207 @.> Date 07/08/2024 10:37 To Coobiw/IP-IQA @.> Cc Subscribed @.***> Subject Re: [Coobiw/IP-IQA] The results are different from the article (Issue #2)

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"val_qual_agg": 2.400309284346071, "val_qual_PLCC": 0.8918036968985772, "val_qual_SROCC": 0.8459583982962504, "val_qual_KROCC": 0.6625471891512434, "val_align_agg": 1.9544182865825195, "val_align_PLCC": 0.8049642910060447, "val_align_SROCC": 0.6630355658985246, "val_align_KROCC": 0.4864184296779501, "val_best_epoch": 74} {"train_lr": "0.000010", "train_loss": "0.032561"} {"val_agg_metrics": 4.368401910228729, "val_qual_agg": 2.399263884905551, "val_qual_PLCC": 0.893940038168873, "val_qual_SROCC": 0.8451494037226237, "val_qual_KROCC": 0.6601744430140543, "val_align_agg": 1.969138025323178, "val_align_PLCC": 0.8104682448379512, "val_align_SROCC": 0.668304226340349, "val_align_KROCC": 0.49036555414487787, "val_best_epoch": 74} {"train_lr": "0.000010", "train_loss": "0.030498"} {"val_agg_metrics": 4.362395323342165, "val_qual_agg": 2.3893576838788007, "val_qual_PLCC": 0.8918653298159165, "val_qual_SROCC": 0.8405790313321705, "val_qual_KROCC": 0.6569133227307137, "val_align_agg": 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"val_qual_KROCC": 0.6544843503817428, "val_align_agg": 1.9956006274629243, "val_align_PLCC": 0.8169585603404932, "val_align_SROCC": 0.6773797505603084, "val_align_KROCC": 0.501262316562123, "val_best_epoch": 97} {"train_lr": "0.000010", "train_loss": "0.024417"} {"val_agg_metrics": 4.404147390995561, "val_qual_agg": 2.3962038303515425, "val_qual_PLCC": 0.8944198198003533, "val_qual_SROCC": 0.8434088063141504, "val_qual_KROCC": 0.6583752042370389, "val_align_agg": 2.0079435606440184, "val_align_PLCC": 0.8218869397979285, "val_align_SROCC": 0.6810495964563689, "val_align_KROCC": 0.505007024389721, "val_best_epoch": 97} this is my log — Reply to this email directly, view it on GitHub, or unsubscribe. 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Coobiw commented 4 months ago

But your result is still lower than mine, in spite of the lack of Image2Prompt Continue Pretraining. I will give you my recorded results which shows the quaility score. Additionally, I find that you cut down the batch-size, you may try to follow the linear rule of batchsize-lr or set the image resolution to 224(If not Image2Prompt pretrained, 224-clip-resnet is also a good choice).

One thing important is that, you should use 10-fold cross-validation.

Without Image2Prompt Pretrained CLIP-ResNet50 Log:

[
    {
        "agg_metrics": 0.8851561978676274,
        "PLCC": 0.9070178136580876,
        "SROCC": 0.8632945820771671,
        "KROCC": 0.6812016744644063,
        "MainScore": 0.8851561978676274
    },
    {
        "agg_metrics": 0.8862317211099505,
        "PLCC": 0.9068168438907296,
        "SROCC": 0.8656465983291715,
        "KROCC": 0.6873998815118637,
        "MainScore": 0.8862317211099505
    },
    {
        "agg_metrics": 0.8925108766527425,
        "PLCC": 0.9193916467414135,
        "SROCC": 0.8656301065640715,
        "KROCC": 0.6864398170470329,
        "MainScore": 0.8925108766527425
    },
    {
        "agg_metrics": 0.8889483299135066,
        "PLCC": 0.909199923386543,
        "SROCC": 0.8686967364404702,
        "KROCC": 0.6912725429635603,
        "MainScore": 0.8889483299135066
    },
    {
        "agg_metrics": 0.8933460202180137,
        "PLCC": 0.9157552175792577,
        "SROCC": 0.8709368228567695,
        "KROCC": 0.6940111612265154,
        "MainScore": 0.8933460202180137
    },
    {
        "agg_metrics": 0.8748107652951171,
        "PLCC": 0.9030332492357808,
        "SROCC": 0.8465882813544536,
        "KROCC": 0.667505412202475,
        "MainScore": 0.8748107652951171
    },
    {
        "agg_metrics": 0.9026681778530823,
        "PLCC": 0.9206449925285352,
        "SROCC": 0.8846913631776295,
        "KROCC": 0.7099482354924073,
        "MainScore": 0.9026681778530823
    },
    {
        "agg_metrics": 0.8783247476843916,
        "PLCC": 0.9070863600794715,
        "SROCC": 0.8495631352893118,
        "KROCC": 0.6672270829285575,
        "MainScore": 0.8783247476843916
    },
    {
        "agg_metrics": 0.9015773673364125,
        "PLCC": 0.9233246909674946,
        "SROCC": 0.8798300437053302,
        "KROCC": 0.7036759401750424,
        "MainScore": 0.9015773673364125
    },
    {
        "agg_metrics": 0.8935181993281476,
        "PLCC": 0.9185850390900239,
        "SROCC": 0.8684513595662712,
        "KROCC": 0.6916901710287316,
        "MainScore": 0.8935181993281476
    },
    {
        "total_metric": {
            "agg_metrics": 0.8897092403258993,
            "PLCC": 0.9130855777157338,
            "SROCC": 0.8663329029360647,
            "KROCC": 0.6880371919040593,
            "MainScore": 0.8897092403258993
        }
    }
]
ctxya1207 commented 4 months ago

但是，尽管没有Image2Prompt继续进行预训练，您的结果仍然低于我的。 我将向您提供我记录的结果，其中显示了质量分数。此外，我发现您减少了批量大小，您可以尝试遵循linear rule of batchsize-lr或将图像分辨率设置为 224（如果没有Image2Prompt进行预训练，224-clip-resnet 也是一个不错的选择）。

重要的一点是，您应该使用 10 倍交叉验证。

没有Image2Prompt预训练的 CLIP-ResNet50 日志：

[
    {
        "agg_metrics": 0.8851561978676274,
        "PLCC": 0.9070178136580876,
        "SROCC": 0.8632945820771671,
        "KROCC": 0.6812016744644063,
        "MainScore": 0.8851561978676274
    },
    {
        "agg_metrics": 0.8862317211099505,
        "PLCC": 0.9068168438907296,
        "SROCC": 0.8656465983291715,
        "KROCC": 0.6873998815118637,
        "MainScore": 0.8862317211099505
    },
    {
        "agg_metrics": 0.8925108766527425,
        "PLCC": 0.9193916467414135,
        "SROCC": 0.8656301065640715,
        "KROCC": 0.6864398170470329,
        "MainScore": 0.8925108766527425
    },
    {
        "agg_metrics": 0.8889483299135066,
        "PLCC": 0.909199923386543,
        "SROCC": 0.8686967364404702,
        "KROCC": 0.6912725429635603,
        "MainScore": 0.8889483299135066
    },
    {
        "agg_metrics": 0.8933460202180137,
        "PLCC": 0.9157552175792577,
        "SROCC": 0.8709368228567695,
        "KROCC": 0.6940111612265154,
        "MainScore": 0.8933460202180137
    },
    {
        "agg_metrics": 0.8748107652951171,
        "PLCC": 0.9030332492357808,
        "SROCC": 0.8465882813544536,
        "KROCC": 0.667505412202475,
        "MainScore": 0.8748107652951171
    },
    {
        "agg_metrics": 0.9026681778530823,
        "PLCC": 0.9206449925285352,
        "SROCC": 0.8846913631776295,
        "KROCC": 0.7099482354924073,
        "MainScore": 0.9026681778530823
    },
    {
        "agg_metrics": 0.8783247476843916,
        "PLCC": 0.9070863600794715,
        "SROCC": 0.8495631352893118,
        "KROCC": 0.6672270829285575,
        "MainScore": 0.8783247476843916
    },
    {
        "agg_metrics": 0.9015773673364125,
        "PLCC": 0.9233246909674946,
        "SROCC": 0.8798300437053302,
        "KROCC": 0.7036759401750424,
        "MainScore": 0.9015773673364125
    },
    {
        "agg_metrics": 0.8935181993281476,
        "PLCC": 0.9185850390900239,
        "SROCC": 0.8684513595662712,
        "KROCC": 0.6916901710287316,
        "MainScore": 0.8935181993281476
    },
    {
        "total_metric": {
            "agg_metrics": 0.8897092403258993,
            "PLCC": 0.9130855777157338,
            "SROCC": 0.8663329029360647,
            "KROCC": 0.6880371919040593,
            "MainScore": 0.8897092403258993
        }
    }
]

thank your answer，Can you provide me with this weight, because I want to reproduce the results in the paper, just for academic research, if used, will be cited

ctxya1207 commented 4 months ago

agg_metrics: 4.405825565927749 qual_agg: 2.408340490883856 qual_PLCC: 0.8991440667380974 qual_SROCC: 0.8464781185052737 qual_KROCC: 0.6627183056404847 align_agg: 1.997485075043894 align_PLCC: 0.8219373830867781 align_SROCC: 0.676558703099623 align_KROCC: 0.49898898885749254，this is my result of 10-fold cross validation

Coobiw commented 4 months ago

The result above is not equipped with Image2Prompt Continue Pretraining, running with the config https://github.com/Coobiw/IP-IQA/blob/master/ipiqa/projects/agiqa3k/ipiqa_224.yaml. As for the pretrained weight of Image2Prompt, it may not be released for the time being due to some reasons which are inconvenient to say.