hkchengrex/Cutie-onnx
AI model by hkchengrex
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Model card & documentation
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The released small Cutie model (cutie-small-mega.pth, ResNet-18 encoders; CVPR 2024), converted to ONNX so it runs in a web page with ONNX Runtime Web. It powers the live demo in the blog post Cutie: object queries that clean up pixel matching. The pixel memory, the object memory, and the frame loop are not in these graphs; they live in model.js, a line-by-line port of cutie/inference/inferencecore.py and memorymanager.py for one object, with the evaluation settings (memory every 5 frames, at most 5 memory frames, top-30 matching, no long-term memory). The graphs keep their initializers in external data (.data, .f16.data), which are not shipped: the page rebuilds them from weights.bin and passes them to InferenceSession.create as externalData. That way the fp32 and fp16 graphs share one 28 MB download. Use ONNX Runtime Web 1.30 or later: 1.22 has no…
Read the full model card ↗ · Preview checked 2026-10-29T05:31:50.630Z
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Model overview
hkchengrex/Cutie-onnx is a task-unspecified model repository published by hkchengrex on Hugging Face. The source reports the onnxruntime library.
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Model facts
- Task
- Not reported
- Library
- onnxruntime
- Recent downloads (30 days)
- 0
- Cumulative likes
- 0
- Hugging Face trending score
- 0
- Reported safetensors parameters
- Not reported
- Architecture
- Not reported
- License
- mit
- Access
- Not gated by Hugging Face
- Created
- 2026-10-20T06:41:50.000Z
- Last modified
- 2026-10-20T06:42:23.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-10-20T06:43:37.702Z. Daily imports are snapshots, not real-time monitoring.
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