efficientvit_l2.r288_in1k
An EfficientViT (MIT) image classification model. Trained on ImageNet-1k by paper authors.
Params
64 M
Context
—
Downloads 30d
1.3 M
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daily snapshots · 7 days1.3 M377 K
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Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f32 | 0.3 GB | 0.8 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Parameters
- 64 M
- Tensor type
- F32
- Licence
- apache-2.0
- First seen on the Hub
- 2023-11-21
- Training datasets
- imagenet-1k
- Added to our catalog
- 2026-11-04
Compare with any image-classification model