dsh-vision-tiler
面向 DeepSeek Harness 视觉模型的损失感知、全覆盖图像平铺
插件会安装到这里；不确定时保持 web。
npx -y @deepseek-ai/dsh plugin --profile web add github:zyh20041227/improved_vision_for_deepseek#cbcf80280074082e7299daddeeaa1542385e372cVision Tiler 以 dsh-vision-tiler 发布，当前版本为 0.2.3。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。
中文说明 · Technical report · Dense-text benchmark
Full-coverage image tiling for DeepSeek Harness (DSH) vision models. The plugin turns one high-resolution image into a global overview, overlapping coverage tiles, and an optional dense-region detail crop before the model reads it.
Dense-text benchmark comparison
DeepSeek documents a maximum of 384 vision tokens per image and scales large images before inference. That budget is often enough for ordinary photos, but it can remove small characters from receipts, tables, diagrams, and long screenshots. DSH Vision Tiler gives each local region its own image budget while preserving a complete, auditable view of the source.
sharp/libvips conflicts inside DSH Web.Requirements: DeepSeek Harness, a vision-capable DSH model profile, and Node.js 22 or later.
Pinned GitHub release:
dsh plugin --profile web add github:zyh20041227/improved_vision_for_deepseek#v0.2.3
dsh --profile web --dump-config
No allow-build entry is required. Runtime packages are declared in package.json and locked in package-lock.json; these files are the Node.js equivalent of Python's requirements.txt. An npm registry release is planned but is not yet published, so use the pinned GitHub command above.
DSH Web showing Vision Tiler enabled
Ask the model to call the registered segment_image tool and continue through every returned batch:
Call segment_image for D:\images\document.png with mode=document and batch_index=0.
If remaining_batch_indices is not empty, read every remaining batch before answering.
Report uncertain_regions and cite the tile IDs used.
| Argument | Meaning |
|---|---|
path | Absolute path, or a path relative to the DSH process directory |
mode | auto, document, diagram, or photo |
strategy | adaptive (default) or uniform (control mode) |
batch_index | Zero-based output batch |
The DSH profile must use a model that accepts image attachments. A text-only route can run the tiler, but it cannot pass the resulting images to the model.
The controlled dense-text benchmark contains four synthetic pages with 100 unique eight-character codes each. Every model arm read each page independently three times: 12 calls and 1,200 exact-code decisions per arm.
| Configuration | Exact-code F1 | Median latency | Mean total tokens/call | Estimated cost/call |
|---|---|---|---|---|
| GPT-5.5 | 99.25% | 27.95 s | Not exposed | Codex subscription; not convertible |
| GPT-5.6 Terra | 98.67% | 25.17 s | Not exposed | Codex subscription; not convertible |
| DeepSeek + plugin | 96.44% | 6.08 s | 4,970.8 | ¥0.004521 observed-cache estimate |
| GPT-5.6 Luna | 94.99% | 27.48 s | Not exposed | Codex subscription; not convertible |
| DeepSeek direct image | 19.68% | 6.87 s | 1,135.5 | ¥0.001830 estimate |
For this task, tiling increased the estimated DeepSeek charge per call by about 2.47×, but reduced estimated cost per 100 correct codes by about 51%. DeepSeek's experimental vision model has no separate public price row, so these values use the published V4 Flash rates and are estimates, not invoices. Codex does not expose per-task vision tokens or billable API cost here, so GPT prices are intentionally not guessed.
It can, especially when a large image contains small, low-contrast, or tightly packed text. In the controlled test, the model and prompt stayed the same while the input changed from one scaled image to complete local tiles; F1 rose from 19.68% to 96.44%. This is strong engineering evidence for that workload, not a claim that every image needs tiling.
The plugin guarantees geometric pixel coverage. It cannot guarantee that a model semantically recognises every visible character; blurred input, compression artefacts, unusual fonts, and model errors still require review.
The public repository contains aggregate results and reproducible generators, but never API keys or local caches.
npm install
npm test
npm pack
The test suite covers exact geometric coverage, seam overlap, safety caps, deterministic batching, adaptive detail selection, WebP/WASM decoding, EXIF orientation, and DSH tool rendering. See CONTRIBUTING.md and SECURITY.md.
MIT