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A PyTorch Implementation of Neural IMage Assessment
| Date | Stars |
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| 2026-07-24 | 584 |
| 2026-07-25 | 584 |
| 2026-07-28 | 584 |
| 2026-07-30 | 585 |
| 2026-08-06 | 585 |
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## NIMA: Neural IMage Assessment [](https://www.python.org/) [](./LICENSE) This is a PyTorch implementation of the paper [NIMA: Neural IMage Assessment](https://arxiv.org/abs/1709.05424) (accepted at [IEEE Transactions on Image Processing](https://ieeexplore.ieee.org/document/8352823)) by Hossein Talebi and Peyman Milanfar. You can learn more from [this post at Google Research Blog](https://research.googleblog.com/2017/12/introducing-nima-neural-image-assessment.html). ## Implementation Details + The model was trained on the [AVA (Aesthetic Visual Analysis) dataset](http://refbase.cvc.uab.es/files/MMP2012a.pdf) containing 255,500+ images. You can get it from [here](https://github.com/mtobeiyf/ava_downloader). ~~**Note: there may be some corrupted images in the dataset, remove them first before you start training**.~~ Use provided CSVs which have already done this for you. + Dataset is split into 229,981 images for training, 12,691 images for validation and 12,818 images for testing. + An ImageNet pretrained VGG-16 is used as the base network. Should be easy to plug in the other two options (MobileNet and Inception-v2). + The learning rate setting differs from the original paper. Can't seem to get the model to converge using the original params. Also didn't do much hyper-param tuning therefore you could probably get better results. Other settings are all directly mirrored from the paper. ## Requirements Code is written using [PyTorch](https://pytorch.org/get-started/locally/) 1.8.1 with [CUDA](https://developer.nvidia.com/cuda-toolkit) 11.1. You can recreate the environment I used with [conda](https://docs.conda.io/en/latest/miniconda.html) by ``` conda env create -f env.yml ``` to install the dependancies. ## Usage To start training on the AVA dataset, first download the dataset from the link above and decompress which should create a directory named ```images/```. Then download the curated annotation CSVs below which already splits the dataset (You can create your own split of course). Then do ```python python main.py --img_path /path/to/images/ --train --train_csv_file /path/to/train_labels.csv --val_csv_file /path/to/val_labels.csv --conv_base_lr 5e-4 --dense_lr 5e-3 --decay --ckpt_path /path/to/ckpts --epochs 100 --early_stoppping_patience 10 ``` For inference, do ```python python -W ignore test.py --model /path/to/your_model --test_csv /path/to/test_labels.csv --test_images /path/to/images --predictions /path/to/save/predictions ``` See ```predictions/``` for dumped predictions as an example. ## Training Statistics Training is done with early stopping. Here I set ```early_stopping_patience=10```. <p align="center"> <img src="./snapshots/[email protected]"> </p> ## Pretrained Model ~0.069 EMD on validation. Not fully converged yet (constrained by resources). To continue training, download the pretrained weights and add ```--warm_start --warm_start_epoch 34``` to your args. [Google Drive](https://drive.google.com/file/d/1w9Ig_d6yZqUZSR63kPjZLrEjJ1n845B_/view?usp=sharing) ## Annotation CSV Files [Train](https://drive.google.com/file/d/1IBXPXPkCiTz04wWcoReJv4Nk06VsjSkI/view?usp=sharing) [Validation](https://drive.google.com/file/d/1tJfO1zFBoQYzd8kUo5PKeHTcdzBL7115/view?usp=sharing) [Test](https://drive.google.com/file/d/105UGnkglpKuusPhJaPnFSa2JlQV3du9O/view?usp=sharing) ## Example Results + Here first shows some good predictions from the test set. Each image title starts with ground-truth rating followed by the predicted mean and std in the parentheses. <p align="center"> <img src="./snapshots/goodpred.png"> </p> + Also some failure cases, it would seem that the model usually fails at images with low/high aesthetic ratings. <p align="center"> <img src="./snapshots/badpred.png"> </p> + The predicted aesthetic ratings from training on the AVA dataset are sensitive to co
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2302a9eeeede4edd, topic:computer-vision