Update README.md

README.md

@@ -25,13 +25,7 @@ For the classifier models, the final output goes through `nn.Softmax`.
 
 # Models
 
-## Future/planned
-
-- Unified (by joining the datasets of the other classifiers)
-- Compression (jpg/webp/gif/dithering/etc)
-- Noise
-
-## ChromaticAberration - Anime
+## Chromatic Aberration - Anime
 
 ### Design goals
 
@@ -74,3 +68,54 @@ Version history:
 - 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).
+
+## Image Compression - Anime
+
+### Design goals
+
+The goal was to detect [compression artifacts](https://en.wikipedia.org/wiki/Compression_artifact?useskin=vector) in images.
+
+This seems like the next logical step in dataset filtering. The flagged images can either be cleaned up or tagged correctly so the resulting network won't inherit the image artifacts.
+
+### Issues
+
+- Low accuracy on 3D/2.5D with possible false positives.
+
+### Training
+
+The training settings can be found in the `config/CCAnime-Compression-v1.yaml` file (2.7e-6 LR, cosine scheduler, 40K steps).
+
+![loss](https://github.com/city96/CityClassifiers/assets/125218114/9d0294bf-81ee-4b30-89ae-3b1aca27788e)
+
+The eval loss only uses a single image for each target class, hence the questionable nature of the graph.
+
+![loss-eval](https://github.com/city96/CityClassifiers/assets/125218114/77c9882f-6263-4926-b3ee-a032ef7784ea)
+
+
+Final dataset score distribution for v1.5:
+```
+22736 images in dataset.
+0_fpl      -  108
+0_reg_aes  -  142
+0_reg_gel  - 7445 |||||||||||||
+1_aes_jpg  -  103
+1_fpl      -    8
+1_syn_gel  - 7445 |||||||||||||
+1_syn_jpg  -   40
+2_syn_gel  - 7445 |||||||||||||
+2_syn_webp -    0
+
+Class ratios:
+00 - 7695 |||||||||||||
+01 - 7596 |||||||||||||
+02 - 7445 |||||||||||||
+```
+
+Version history:
+
+- v1.0 - Initial test model, dataset consists of 40 hand picked images and their jpeg compressed counterpart. Compression is done with ChaiNNer, compression rate is randomized.
+- v1.1 - Added more images by re-filtering the input dataset using the v1 model, keeping only the top/bottom 10%.
+- 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 - Scraped ~7500 images from gelbooru, filtering for min. image size of at least 3000 and a file size larger than 8MB. Compressed using ChaiNNer as before.
+- v1.4 - Added webm compression to the list, decided against adding GIF/dithering since it's rarely used nowadays.
+- v1.5 - Changed LR/step count to better match larger dataset. Added false positives/negatives from v1.4.