|
Download eval/README.md from InfoVis/VisSlideBench: direct link, hf CLI and curl.
- Browser
- Download file 2.28 kB
-
https://huggingface.co/datasets/InfoVis/VisSlideBench/resolve/main/eval/README.md
- Command line
-
hf download hf://datasets/InfoVis/VisSlideBench/eval/README.md
-
curl -L -o README.md https://huggingface.co/datasets/InfoVis/VisSlideBench/resolve/main/eval/README.md
2.28 kB
| # VisSlideBench evaluation | |
| ## Setup | |
| ```bash | |
| pip install -r eval/requirements.txt # judge + text matching | |
| pip install paddlepaddle-gpu paddleocr==3.3.2 # OCR, only for run_ocr.py | |
| export OPENAI_API_KEY=... # any OpenAI-compatible endpoint | |
| export OPENAI_BASE_URL=... # optional | |
| export VISSLIDEBENCH_JUDGE=gpt-4.1 # optional, default gpt-4.1 | |
| ``` | |
| ## Predictions | |
| Generate one image per case and save it as `<pred_dir>/<track>/<id>.png`, | |
| where `<track>` is `t2i`, `template`, `multi_image`, or `edit` and `<id>` is the | |
| `id` field. Our runs generate t2i cases at 1280x720, template and multi_image | |
| cases at 1376x768, and edit cases at the record's `width` and `height` (the | |
| source slide size), with seed `2233 + index` for case `index`. | |
| ## Scoring | |
| ```bash | |
| cd VisSlideBench | |
| for t in t2i template multi_image; do | |
| python eval/run_ocr.py --task $t --pred_dir $PRED --device gpu:0 | |
| python eval/score_generation.py --task $t --pred_dir $PRED --workers 16 | |
| done | |
| python eval/score_edit.py --pred_dir $PRED --workers 16 | |
| python eval/aggregate.py --pred_dir $PRED # Table 1 layout | |
| python eval/aggregate.py --pred_dir $PRED --by source # real vs synthetic | |
| python eval/aggregate.py --pred_dir $PRED --by prompt_type # short / long / style_json | |
| ``` | |
| Per-case scores go to `<pred_dir>/scores/<track>.jsonl`. A case that fails a | |
| metric (missing image, no OCR text, no required text in the instruction, or a | |
| judge error after three retries) has no value for that metric and is left out | |
| of that metric's average. | |
| ## Judge settings | |
| | Call | Prompt | Temperature | Max tokens | | |
| |---|---|---:|---:| | |
| | Required-text extraction (CF) | in `score_generation.py` | 0.2 | 800 | | |
| | Input-image preservation (multi_image CF) | `prompts/content_faithfulness.txt` | 0.2 | 1200 | | |
| | Instruction Following | `prompts/style_instruction_following_{text,json}.txt` | 0.2 | 2000 | | |
| | Aesthetic Quality | `prompts/aesthetic_quality.txt` | 0.2 | 2000 | | |
| | Edit success + consistency | `prompts/edit_evaluation_source_control.txt` | 0.7 | 512 | | |
| | Edit presentation quality | `prompts/edit_evaluation_quality.txt` | 0.7 | 512 | | |
| Images sent to the judge are JPEG-encoded with the long side capped at 1,600 | |
| pixels and `detail: high`. | |