Model overview
Binary image classifier: `rain` vs `no_rain`, for a camera looking out a dock door. Fine-tuned from facebook/convnext-tiny-224 (Apache-2.0). Part of Kairos, a weather plugin for Boston Dynamics Spot fleets (Knight Hacks IX hackathon build). It feeds one input of a GO / HOLD rule. Python: `pipeline("image-classification", model="builtbyd3v/kairos-door-rain")`. Hold when p(rain) >= **0.4447** for q8, or >= **0.4728** for fp32 (each picked on the calibration split, one image per call). Both are in `eval_results.json` under `thresholds`. 95% CIs are Wilson score intervals. Threshold rule: the highest threshold that keeps calibration rain recall >= 95%, rounded down. Scored with onnxruntime CPU on the shipped ONNX files, one image per call (as in the browser).
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Sources & traceability
许可证：cc-by-4.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。