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| | license: apache-2.0 |
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| | # Anime Classifiers |
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| | [Training/inference code](https://github.com/city96/CityClassifiers) | [Live Demo](https://huggingface.co/spaces/city96/AnimeClassifiers-demo) |
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| | These are models that predict whether a concept is present in an image. The performance on high resolution images isn't very good, especially when detecting subtle image effects such as noise. This is due to CLIP using a fairly low resolution (336x336/224x224). |
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| | To combat this, tiling is used at inference time. The input image is first downscaled to 1536 (shortest edge - See `TF.functional.resize`), then 5 separate 512x512 areas are selected (4 corners + center - See `TF.functional.five_crop`). This helps as the downscale factor isn't nearly as drastic as passing the entire image to CLIP. As a bonus, it also avoids the issues with odd aspect ratios requiring cropping or letterboxing to work. |
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| | As for the training, it will be detailed in the sections below for the individual classifiers. At first, specialized models will be trained to a relatively high accuracy, building up a high quality but specific dataset in the process. |
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| | Then, these models will be used to split/sort each other's the datasets. The code will need to be updated to support one image being part of more than one class, but the final result should be a clean dataset where each target aspect acts as a "tag" rather than a class. |
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| | ## Architecture |
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| | The base model itself is fairly simple. It takes embeddings from a CLIP model (in this case, `openai/clip-vit-large-patch14`) and expands them to 1024 dimensions. From there, a single block with residuals is followed by a few linear layers which converge down to the final output. |
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| | For the classifier models, the final output goes through `nn.Softmax`. |
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| | # Models |
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| | ## Future/planned |
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| | - Unified (by joining the datasets of the other classifiers) |
| | - Compression (jpg/webp/gif/dithering/etc) |
| | - Noise |
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| | ## ChromaticAberration - Anime |
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| | ### Design goals |
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| | The goal was to detect [chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration?useskin=vector) in images. |
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| | For some odd reason, this effect has become a popular post processing effect to apply to images and drawings. While attempting to train an ESRGAN model, I noticed an odd halo around images and quickly figured out that this effect was the cause. This classifier aims to work as a base filter to remove such images from the dataset. |
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| | ### Issues |
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| | - Seems to get confused by excessive HSV noise |
| | - Triggers even if the effect is only applied to the background |
| | - Sometimes triggers on rough linework/sketches (i.e. multiple semi-transparent lines overlapping) |
| | - Low accuracy on 3D/2.5D with possible false positives. |
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| | ### Training |
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| | The training settings can be found in the `config/CCAnime-ChromaticAberration-v1.yaml` file (7e-6 LR, cosine scheduler, 100K steps). |
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| | Final dataset score distribution for v1.16: |
| | ``` |
| | 3215 images in dataset. |
| | 0_reg - 395 |||| |
| | 0_reg_booru - 1805 |||||||||||||||||||||| |
| | 1_chroma - 515 |||||| |
| | 1_synthetic - 500 |||||| |
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| | Class ratios: |
| | 00 - 2200 ||||||||||||||||||||||||||| |
| | 01 - 1015 |||||||||||| |
| | ``` |
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| | Version history: |
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| | - v1.0 - Initial test model, dataset is fully synthetic (500 images). Effect added by shifting red/blue channel by a random amount using chaiNNer. |
| | - v1.1 - Added 300 images tagged "chromatic_aberration" from gelbooru. Added first 1000 images from danbooru2021 as reg images |
| | - v1.2 - Used the newly trained predictor to filter the existing datasets - found ~70 positives in the reg set and ~30 false positives in the target set. |
| | - v1.3-v1.16 - Repeatedly ran predictor against various datasets, adding false positives/negatives back into the dataset, sometimes running against the training set to filter out misclassified images as the predictor got better. Added/removed images were manually checked (My eyes hurt). |
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