| ---
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| license: apache-2.0
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| language:
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| - en
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| base_model: SmilingWolf/wd-eva02-large-tagger-v3
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| base_model_relation: finetune
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| pipeline_tag: image-classification
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| tags:
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| - lighting
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| - image-tagging
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| - multi-label-classification
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| - anime
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| - illustration
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| - booru
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| - danbooru
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| - e621
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| - wd-tagger
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| - eva02
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| - dataset-captioning
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| - distillation
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| ---
|
|
|
| # Booru Lighting Tagger v2.0.0
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|
|
| Booru Lighting Tagger adds **lighting tags** to anime-style illustrations: where the light comes from, how
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| contrasted, soft or colored it is, which light effects and shadows show, and what lights the scene. It
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| outputs 53 booru-style tags (`backlighting`, `rim_light`, `chiaroscuro`, `bloom`, `caustics`,
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| `dappled_sunlight`, `candlelight`…) and a tonal key (`dark`, `dim_lighting`, `high_key`).
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|
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| 
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| 
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| 
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| 
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|
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| *Outputs of v2.0.0 at the default thresholds, on four AI-generated illustrations that are not part of the
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| training data. Each bar is the score out of 10; the white tick is the label's threshold. Grey bars are labels
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| that are not validated against human answers (see [Labels](#labels)). The last line lists the tags that the
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| `recall` preset would add.*
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|
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| Booru tags describe what is in an image much better than how it is lit. Most images with a strong
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| backlight or a dramatic chiaroscuro are not tagged as such, so generative models trained on these captions
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| have no reliable handle on lighting. This tagger is meant to complete the captions of image datasets, so
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| that lighting becomes something you can prompt for.
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|
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| - **Base model**: [WD EVA02-Large Tagger v3](https://huggingface.co/SmilingWolf/wd-eva02-large-tagger-v3)
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| by SmilingWolf. 16 of its 24 transformer blocks were fine-tuned, under a new lighting head.
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| - **Teacher**: Claude Opus 5.5 rated about 25,400 images from 0 to 10 on each label, following a written
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| guideline; the tagger learned these ratings.
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| - **Checked against a human**: on blind human answers, the tagger ranks images better than its teacher
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| (mean AP 0.85 against 0.77 on the 51 labels measured). 38 of the 53 labels reach 70 % precision and
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| 30 % recall (41 for the teacher).
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| - **New in 2.0**: 14 more labels, among them `soft_lighting`, `hard_lighting`, `bloom`, `light_rays`,
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| `vignetting` and the colored lights, a revised guideline, and about 6,000 more training images. Tags of
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| v1.x and v2.x are not consistent with each other: see [Versioning](#versioning).
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| - **License**: Apache 2.0, like the base model.
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|
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| The project report, with the goal, method, experiments, results and limitations, is in
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| [REPORT.md](REPORT.md).
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|
|
| ## Quick start
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|
|
| ```bash
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| pip install -U huggingface_hub # provides the hf command
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| hf download MarioTheKnight/booru-lighting-tagger --local-dir booru-lighting-tagger
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| pip install -r booru-lighting-tagger/requirements.txt
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| python booru-lighting-tagger/inference.py my_image.png
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| ```
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|
|
| ```
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| my_image.png: sunlight, rim_light, hard_lighting, sunset, bloom, diffraction_spikes, golden_hour, backlighting
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| ```
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|
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| At the first run, the base model (1.2 GB) is downloaded from the Hub and cached. If you need a specific
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| CUDA build of PyTorch, install it first ([pytorch.org](https://pytorch.org/get-started/locally/)).
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|
|
| ### Tag a dataset
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|
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| ```bash
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| # Add the missing lighting tags to each image's .txt caption (the file is created if needed)
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| python booru-lighting-tagger/inference.py dataset/ --recursive --append-to-captions
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|
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| # Same, with spaces instead of underscores ("rim light")
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| python booru-lighting-tagger/inference.py dataset/ -r --append-to-captions --spaces
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|
|
| # Keep every score, to apply your own thresholds
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| python booru-lighting-tagger/inference.py dataset/ -r --jsonl lighting.jsonl
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| ```
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| | Option | Effect |
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| |---|---|
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| | `-r`, `--recursive` | Look into subfolders. |
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| | `--append-to-captions [EXT]` | Add the missing tags at the end of `<image name>.txt` (or `EXT`), comma-separated. Existing tags are kept and never duplicated (`rim_light` and `rim light` count as the same tag). |
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| | `--write-captions [EXT]` | Write the lighting tags alone to `<image name>.lighting.txt` (or `EXT`), replacing that file. |
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| | `--jsonl FILE` | Write every result to a JSON Lines file: all scores, tonal key probabilities, tags. |
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| | `--spaces` | Write `rim light` instead of `rim_light`. |
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| | `--preset recall` | More tags: about 1.5 times as many, a little less precise (see [Threshold presets](#threshold-presets)). |
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| | `--validated-only` | Emit only the 38 validated labels (see [Labels](#labels)). |
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| | `--no-tonal-key` | Do not emit `dark`, `dim_lighting` or `high_key`. |
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| | `--seeds N` | Use only the first N of the 3 seeds: faster, slightly less accurate. |
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| | `--repo ID`, `--revision v2.0.0` | Download the model from the Hub instead of reading it next to the script. |
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| | `--device`, `--precision`, `--batch-size`, `--workers` | Hardware settings (`--help` for details). |
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| Formats read: PNG, JPEG, WebP, BMP, GIF (first frame), TIFF. An unreadable file is reported and skipped;
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| the run goes on and exits with code 2.
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|
|
| ### Python
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|
|
| ```python
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| import sys
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| from huggingface_hub import snapshot_download
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|
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| model_dir = snapshot_download("MarioTheKnight/booru-lighting-tagger", revision="v2.0.0")
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| sys.path.insert(0, model_dir)
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| from inference import LightingTagger
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| tagger = LightingTagger(model_dir) # device="auto", seeds=None (all 3), preset="default"
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| for result in tagger.tag_files(["a.png", "b.jpg"], batch_size=8):
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| print(result["file"], result["tags"])
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|
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| from PIL import Image
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| result = tagger.tag_images([Image.open("a.png")])[0]
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| print(result["scores"]["rim_light"]) # 0 to 10
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| result = tagger.tag_images([Image.open("a.png")], preset="recall")[0] # more tags, for this call only
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| ```
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|
|
| ### Output
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|
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| One result per image (abridged; the beach image above):
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|
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| ```json
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| {
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| "file": "beach.png",
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| "tags": ["sunlight", "rim_light", "hard_lighting", "sunset", "bloom", "diffraction_spikes", "golden_hour", "backlighting"],
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| "scores": {"backlighting": 4.01, "rim_light": 5.36, "sidelighting": 0.15, "...": "..."},
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| "tonal_key": "normal",
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| "tonal_key_probabilities": {"dark": 0.0, "dim_lighting": 0.0, "normal": 1.0, "high_key": 0.0},
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| "error": null
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| }
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| ```
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|
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| - **`scores`**: for each of the 53 labels, the rating the teacher would most likely give, from 0 (absent)
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| to 10 (obvious and dominant). It is not a probability. On the teacher's scale, 3 means "hesitant,
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| rather no", 4 to 6 "real but subtle or secondary", 7 or 8 "clearly present". The full scale is in
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| [REPORT.md](REPORT.md#the-teacher-claude-opus-55).
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| - **`tags`**: the labels whose score reaches their threshold, highest score first, then the tonal key
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| unless it is `normal`. By default, each threshold makes the tagger emit as many tags as the teacher did
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| on the validation images. Thresholds are listed in `tagger_config.json`.
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|
|
| ### Threshold presets
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|
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| The teacher is cautious: when a tag is emitted it is usually right, but many images that deserve it are
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| missed. The `recall` preset (`--preset recall`, or `preset="recall"` in Python) lowers the thresholds to
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| emit about 1.5 times as many tags as the teacher on the validation images. Four labels keep their default
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| threshold, because their precision on the human tests falls below 60 % otherwise: `sidelighting`,
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| `soft_lighting`, `overexposure` and `night`. A recall threshold is never higher than the default one: the
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| recall preset only adds tags.
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|
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| | Preset | Mean precision | Mean recall |
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| |---|---:|---:|
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| | `default` | 89 % | 55 % |
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| | `recall` | 81 to 84 % | 63 % |
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|
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| Means over the labels measured on the human tests. The four exceptions were chosen on these tests, so the
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| recall preset's figures are a little optimistic there: 84 % precision on the tests, 81 % in
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| cross-validation (exceptions chosen on half of the images, measured on the other half). With the recall
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| preset, `silhouette`, `volumetric_lighting`, `colored_lights` and `shadow` reach 70 % precision and 30 %
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| recall. Per-label figures are in [REPORT.md](REPORT.md#threshold-presets). For finer control, keep every
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| score with `--jsonl` and apply your own thresholds.
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|
|
| ### Speed and memory
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|
|
| | | 3 seeds (default) | 1 seed |
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| |---|---|---|
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| | GPU, RTX PRO 6000 Blackwell, batches of 16 | 39 images/s; 3.8 GB of VRAM, 4.6 GB at peak | 85 images/s; 1.3 GB, 2.1 GB at peak |
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| | CPU, 16 threads | about 3 s per image (measured with v1, same architecture) | not measured |
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|
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| On a GPU, the tagger runs in bfloat16 as in training (float16 on GPUs without bfloat16). On a CPU it runs
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| in float32. The thresholds were set for the average of the 3 seeds; with fewer seeds they still apply, a
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| little less precisely (a single seed: mean validation AP 0.725 to 0.728, against 0.745 for the average).
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|
|
| ## Labels
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|
|
| <!-- labels-table:start -->
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| | Family | Tag | What it means | Status | Precision | Recall | Human positives |
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| |---|---|---|---|---:|---:|---:|
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| | Direction | `backlighting` | Main light behind the subject, facing the viewer | validated | 86 % | 59 % | 32 |
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| | Direction | `rim_light` | Bright edge outlining the subject | low recall | 75 % | 11 % | 28 |
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| | Direction | `sidelighting` | Light from one side, the other half in marked shadow | low precision | 50 % | 13 % | 23 |
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| | Direction | `overlighting` | Light from above, with marked downward shadows | validated | 94 % | 34 % | 50 |
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| | Direction | `underlighting` | Light from below (lamp, fire, screen) | validated | 100 % | 35 % | 31 |
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| | Direction | `spotlight` | A targeted beam isolates the subject | validated | 71 % | 83 % | 12 |
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| | Direction | `crack_of_light` | Light entering a dark space through a gap | validated | 100 % | 58 % | 12 |
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| | Contrast | `high_contrast` | Deep blacks against bright highlights, few midtones | validated | 86 % | 44 % | 27 |
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| | Contrast | `low_contrast` | Compressed tones: soft, hazy, washed-out | low recall | 100 % | 14 % | 14 |
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| | Contrast | `chiaroscuro` | Light and shadow structure the whole composition | validated | 100 % | 49 % | 35 |
|
| | Contrast | `silhouette` | Subject reduced to a dark outline against light | low recall | 100 % | 17 % | 18 |
|
| | Quality | `soft_lighting` *(new)* | Volumes modeled by soft, gradual shadows | low precision | 63 % | 74 % | 25 |
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| | Quality | `hard_lighting` *(new)* | Sharp-edged shadows and marked highlights | validated | 90 % | 65 % | 16 |
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| | Quality | `dramatic_lighting` | Light staged for mood, like a film shot | validated | 75 % | 79 % | 42 |
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| | Quality | `volumetric_lighting` | Light visible in the air: mist, dust, beams | low recall | 91 % | 21 % | 48 |
|
| | Quality | `complex_lighting` *(new)* | Two or more distinct sources light the subject | validated | 79 % | 45 % | 10 |
|
| | Color | `warm_lighting` | Clearly warm light tinting the subject | validated | 100 % | 59 % | 17 |
|
| | Color | `cool_lighting` | Clearly cool light tinting the subject | low recall | 100 % | 11 % | 18 |
|
| | Color | `golden_hour` | Low golden sun after sunrise or before sunset | validated | 80 % | 76 % | 21 |
|
| | Color | `colored_lights` *(new)* | Several saturated lights of different colors | low recall | 88 % | 28 % | 17 |
|
| | Color | `red_light` | Saturated red light tinting the scene | validated | 91 % | 74 % | 27 |
|
| | Color | `orange_light` | Saturated orange light tinting the scene | low recall | 100 % | 19 % | 21 |
|
| | Color | `yellow_light` *(new)* | Saturated yellow light tinting the scene | validated | 100 % | 35 % | 20 |
|
| | Color | `green_light` | Saturated green light tinting the scene | low precision | 67 % | 92 % | 13 |
|
| | Color | `blue_light` *(new)* | Saturated blue light tinting the scene | validated | 91 % | 62 % | 16 |
|
| | Color | `purple_light` | Saturated purple light tinting the scene | validated | 88 % | 70 % | 20 |
|
| | Color | `pink_light` *(new)* | Saturated pink light tinting the scene | validated | 91 % | 56 % | 18 |
|
| | Effects | `light_rays` *(new)* | Distinct beams of light crossing the air | validated | 100 % | 54 % | 14 |
|
| | Effects | `light_particles` | Shining particles floating in the air | validated | 82 % | 49 % | 37 |
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| | Effects | `lens_flare` | Lens artifact: streaks, halos, aligned circles | validated | 100 % | 43 % | 14 |
|
| | Effects | `bloom` *(new)* | Blurry glow spilling over nearby edges | validated | 78 % | 78 % | 20 |
|
| | Effects | `overexposure` | Highlights blown out to pure white | does not work | – | 0 % | 24 |
|
| | Effects | `diffraction_spikes` | Star-shaped spikes around a bright source | validated | 91 % | 77 % | 13 |
|
| | Effects | `caustics` | Rippling light patterns cast by water or glass | validated | 94 % | 74 % | 23 |
|
| | Effects | `refraction` | Image bent or shifted through water or glass | validated | 100 % | 54 % | 13 |
|
| | Effects | `dispersion_(optics)` *(new)* | Light split into rainbow colors (prism, crystal) | validated | 94 % | 88 % | 17 |
|
| | Effects | `subsurface_scattering` | Light glowing through skin, leaves, wax | low precision | 50 % | 10 % | 10 |
|
| | Effects | `vignetting` *(new)* | Edges and corners of the image gradually darken | validated | 100 % | 71 % | 20 |
|
| | Shadows | `shadow` | Strong or striking cast shadows | low recall | 100 % | 18 % | 11 |
|
| | Shadows | `shade` *(new)* | Subject in the shade while strong light hits the rest | not measured | 75 % | 38 % | 8 |
|
| | Shadows | `dappled_sunlight` | Patches of light through foliage | validated | 95 % | 86 % | 21 |
|
| | Shadows | `window_shadow` | Light and shadow pattern from a window or blinds | validated | 89 % | 75 % | 32 |
|
| | Shadows | `colored_shadow` *(new)* | Shadows with a clear hue, not just darker | not measured | – | – | – |
|
| | Shadows | `shaded_face` *(new)* | Upper face or eyes in shadow | validated | 91 % | 60 % | 24 |
|
| | Shadows | `partially_shaded_face` | Face split into a lit half and a shaded half | validated | 89 % | 33 % | 24 |
|
| | Sources | `sunlight` | Sunlight emphasized | validated | 100 % | 38 % | 26 |
|
| | Sources | `moonlight` | Pale moonlight at night | validated | 81 % | 100 % | 17 |
|
| | Sources | `candlelight` | Scene lit mostly by candles | validated | 90 % | 95 % | 19 |
|
| | Sources | `fire_light` | Scene lit by the orange glow of a fire | validated | 100 % | 73 % | 26 |
|
| | Sources | `screen_light` | A visible screen lights a character | validated | 88 % | 91 % | 23 |
|
| | Sources | `glowing` | An object or character emits light | validated | 85 % | 74 % | 23 |
|
| | Sources | `night` | Night scene | validated | 92 % | 79 % | 14 |
|
| | Sources | `sunset` | Sun near the horizon, orange or red sky | validated | 100 % | 92 % | 37 |
|
| <!-- labels-table:end -->
|
|
|
| Precision and recall are measured at the tagger's default thresholds against blind answers from one human
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| annotator, on about 1,600 test images never seen in training (near-duplicates included). The test sets are
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| small: the figures of a single label can move by 10 to 20 points.
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|
|
| - **validated** (38 labels): at least 70 % precision and 30 % recall, on at least 10 human positives.
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| `complex_lighting` passes with exactly 10 positives: treat it as fragile.
|
| - **low recall** (8): the tag is usually right when emitted, but it misses many cases. The `recall` preset
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| brings `silhouette`, `volumetric_lighting`, `colored_lights` and `shadow` over the bar, and `rim_light`
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| and `low_contrast` close to it.
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| - **low precision** (4): the tag is emitted too often. `soft_lighting` was kept as a trial: when the
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| guideline was set, the teacher's own ratings never reached 75 % precision against the human. Use it with
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| care.
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| - **does not work** (1): `overexposure` is never emitted at its threshold, and its ranking is weak (AP
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| 0.49). It is kept in the outputs for compatibility; do not rely on it. In `tagger_config.json` its status
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| is `low_recall`.
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| - **not measured** (2): `shade` has only 8 human positives in the tests. `colored_shadow` was not measured
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| against the human on purpose: human answers on colored shadows were not reliable enough to serve as a
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| reference, so the teacher's judgment is the reference for this label.
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|
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| The **tonal key** (`dark`, `dim_lighting`, `normal`, `high_key`) is a single choice per image. It agrees
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| with the teacher on 88 % of the validation images, but has no human reference.
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|
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| The exact definitions, as given to the teacher, are in `tagger_config.json`. They share a few rules: the
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| light is judged, not the drawing style (cel-shading, inking or an overall color grading do not count); an
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| artificial source (screen, candles, fire) or its glow must be visible; the sun and the moon may be off
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| frame.
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|
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| Tag names follow Danbooru where the tag exists there, otherwise e621.
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|
|
| ## How it was made
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|
|
| 1. **Taxonomy.** 53 lighting labels in 7 families, plus the tonal key, each with a written definition.
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| 2. **Teacher.** Claude Opus 5.5 rated each image from 0 to 10 on every label, following a fixed
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| guideline (v2.4). The guideline was revised several times after discussing its disagreements with a
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| human; versions 2.2 to 2.4 recovered the 13 labels that v1 had to leave out and added
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| `dispersion_(optics)`.
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| 3. **Human calibration.** One annotator answered yes or no, blind and one label at a time, on test images.
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| These answers set a threshold per label on the teacher's ratings and serve as the test reference.
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| 4. **Training data.** About 25,400 images from Danbooru, Gelbooru, e621, yande.re and Konachan, all rated
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| by the teacher with guideline v2.4 (about 29 M tokens): the v1 images, rated again, and a new batch of
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| 5,932 images aimed at the weakest labels. Half of the v1 images and a third of the new batch were drawn
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| at random, the rest by booru tag to enrich the rare labels. No explicit images, no AI-generated images,
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| no comic pages. 23,426 images were used for training and 1,946 for validation; images too close to a
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| test image were removed.
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| 5. **Student.** WD EVA02-Large Tagger v3 with its last 16 blocks unfrozen (202 M trainable parameters).
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| For each label, the head predicts P(rating ≥ t) for t = 1 to 10, and the score is their sum. 8 epochs,
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| AdamW, horizontal flips only, since color or crop augmentations would change the lighting. Three seeds,
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| whose scores are averaged.
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| 6. **Thresholds.** For each label, the threshold that gives as many tags as the teacher on the validation
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| images.
|
|
|
| Details, experiments and ablations: [REPORT.md](REPORT.md). An example training script, for reference,
|
| shows the method: [training/README.md](training/README.md).
|
|
|
| ## Results
|
|
|
| | Model | Validation: mean AP against the teacher (53 labels) | Tests: mean AP against the human (51 labels) | Validated labels |
|
| |---|---:|---:|---:|
|
| | One seed alone | 0.725 to 0.728 | not measured | – |
|
| | **This model** (3 seeds averaged) | **0.745** (95 % CI 0.730–0.770) | **0.853** | **38** |
|
| | Teacher (Claude Opus 5.5) | – | 0.769 | 41 |
|
|
|
| Mean validation AP by family: light direction 0.64, contrast 0.76, light quality 0.81, light color 0.72,
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| light effects 0.74, shadows 0.66, sources and time of day 0.91.
|
|
|
| The tagger beats its teacher on the human tests partly because it learns the teacher's average rating,
|
| which smooths out the teacher's run-to-run noise, and because its scores are continuous where the
|
| teacher's ratings are whole numbers. It still validates three labels fewer than the teacher: its default
|
| thresholds copy the teacher's cautious tagging rate, and the teacher's own thresholds were set on these
|
| same human answers, which favors it. See the report for the caveats.
|
|
|
| These figures are **not comparable with v1.x**: the labels, the guideline, the teacher's ratings and the
|
| human references all changed.
|
|
|
| ## Limitations
|
|
|
| - **Domain.** Trained on anime, manga, furry and game-style illustrations from image boards, with a few
|
| hundred 3D renders. Photos, comic pages and AI-generated images were not in the training data, and the
|
| tagger was not measured on them.
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| - **Ratings.** General and sensitive images, plus a few hundred e621 images rated questionable. No explicit
|
| image was used; behavior on explicit images was not measured.
|
| - **Recall over precision.** The default thresholds copy the teacher's tagging rate, which is cautious:
|
| when a tag is emitted it is usually right, but many images that deserve it are missed. Use
|
| `--preset recall`, or `--jsonl` and your own thresholds, if you need more recall.
|
| - **It imitates a teacher.** The tagger reproduces the judgment of Claude Opus 5.5 under one guideline,
|
| including its blind spots. The human reference comes from a single annotator.
|
| - **Weak spots.** Light direction (validation AP 0.64) and shadows (0.66) are the weakest families.
|
| Fifteen labels are not validated: `rim_light`, `low_contrast`, `silhouette`, `volumetric_lighting`,
|
| `cool_lighting`, `colored_lights`, `orange_light` and `shadow` have a low recall; `sidelighting`,
|
| `soft_lighting`, `green_light` and `subsurface_scattering` have a low precision; `overexposure` does not
|
| work; `shade` and `colored_shadow` are not measured against the human.
|
| - **Resolution.** Images are padded to a square and seen at 448 × 448 px. Fine effects (small caustics,
|
| thin rims) can be lost, especially on very tall or wide images.
|
| - **Cost.** Three EVA02-Large copies make it about three times slower than a single tagger; `--seeds 1`
|
| trades a little accuracy for speed.
|
|
|
| ## Intended use
|
|
|
| - Completing the captions of image datasets before training or fine-tuning generative models.
|
| - Searching, filtering or describing a collection of illustrations by lighting.
|
| - Pre-sorting images before a more expensive annotation. This works less well than in v1: on random
|
| validation images, recovering 90 % of the teacher's tags on every label means sending it 79 % of the
|
| images, because `soft_lighting`, `hard_lighting` and `bloom` are frequent. Without these three labels,
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| 52 % of the images are enough.
|
|
|
| It is not meant to judge artistic quality, and it was not designed for photographs.
|
|
|
| ## Versioning
|
|
|
| Versions are `MAJOR.MINOR.PATCH`. MAJOR changes the labels or their meaning, MINOR brings new weights for
|
| the same labels, and PATCH only touches thresholds, scripts or documentation. Version 2.0.0 is a major
|
| version: 14 labels were added and several definitions changed, so captions made with v1.x and v2.x should
|
| not be mixed. Each version is a tag of this repository; pin one with `--revision v2.0.0` or
|
| `revision="v2.0.0"` (`v1.0.2` for the previous model). History: [CHANGELOG.md](CHANGELOG.md).
|
|
|
| ## License and attribution
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|
|
| Apache License 2.0 ([LICENSE](LICENSE)), for the weights, the code and the documentation.
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|
|
| This model is a modified version of
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| [WD EVA02-Large Tagger v3](https://huggingface.co/SmilingWolf/wd-eva02-large-tagger-v3) by SmilingWolf,
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| released under the same license. The changes are listed in [NOTICE](NOTICE). The weight files contain only
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| the fine-tuned parameters; the rest is loaded from the original repository, at the revision used for
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| training.
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|
|
| The training labels were produced with Claude Opus 5.5 by Anthropic. The training images belong to their
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| artists and are not distributed here.
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|
|
| ## Citation
|
|
|
| ```bibtex
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| @misc{booru_lighting_tagger_2026,
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| title = {Booru Lighting Tagger: lighting tags for anime-style illustrations},
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| author = {MarioTheKnight},
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| year = {2026},
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| version = {2.0.0},
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| howpublished = {\url{https://huggingface.co/MarioTheKnight/booru-lighting-tagger}}
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| }
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| ```
|
|
|
