Model overview
A **tiny Vision Transformer trained from scratch** (no pretraining, no ImageNet) that classifies steel surface-defect images into six defect classes. The point of the project is to show a ViT *can* be built for manufacturing and trained on CPU: 64x64 image -> 64 patches of 8x8 -> 2-layer transformer encoder -> class, ~113k parameters, trained on CPU in minutes. Per-class F1 (TinyViT): crazing 85.71% · patches 91.23% · pitted_surface 75.86% · inclusion 68.85% · scratches 68.97% · rolled-in_scale 66.67%. **Honest reading:** the small CNN wins by ~9 points — convolutional inductive bias beats a from-scratch transformer on a 1,440-image dataset. The ViT is still the point of the project: 113k parameters, no pretraining, CPU-trained in ~14 minutes, and 76% is far above the 16.7% chance level. On larger industrial datasets (or with a conv stem) the gap closes; see "What I'd try next" in the notebook.
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。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.
Are these real-time billing prices?
These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
Sources & traceability
许可证：cc-by-sa-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。