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Sponsored by
PoYo.AI
apple / mobileclip_s2_timm
Model's Last Updated: March 01 2025
image-classification
Introduction of mobileclip_s2_timm
Model Details of mobileclip_s2_timm
MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training
MobileCLIP was introduced in MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training (CVPR 2024), by Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel.
This repository contains the MobileCLIP-S2 checkpoint for timm.
Highlights
-
Our smallest variant
MobileCLIP-S0obtains similar zero-shot performance as OpenAI 's ViT-B/16 model while being 4.8x faster and 2.8x smaller. -
MobileCLIP-S2obtains better avg zero-shot performance than SigLIP 's ViT-B/16 model while being 2.3x faster and 2.1x smaller, and trained with 3x less seen samples. -
MobileCLIP-B(LT) attains zero-shot ImageNet performance of 77.2% which is significantly better than recent works like DFN and SigLIP with similar architectures or even OpenAI's ViT-L/14@336 .
Checkpoints
| Model |
# Seen
Samples (B) |
# Params (M)
(img + txt) |
Latency (ms)
(img + txt) |
IN-1k Zero-Shot
Top-1 Acc. (%) |
Avg. Perf. (%)
on 38 datasets |
|---|---|---|---|---|---|
| MobileCLIP-S0 | 13 | 11.4 + 42.4 | 1.5 + 1.6 | 67.8 | 58.1 |
| MobileCLIP-S1 | 13 | 21.5 + 63.4 | 2.5 + 3.3 | 72.6 | 61.3 |
| MobileCLIP-S2 | 13 | 35.7 + 63.4 | 3.6 + 3.3 | 74.4 | 63.7 |
| MobileCLIP-B | 13 | 86.3 + 63.4 | 10.4 + 3.3 | 76.8 | 65.2 |
| MobileCLIP-B (LT) | 36 | 86.3 + 63.4 | 10.4 + 3.3 | 77.2 | 65.8 |
Runs of apple mobileclip_s2_timm on huggingface.co
112
Total runs
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24-hour runs
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3-day runs
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7-day runs
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30-day runs
More Information About mobileclip_s2_timm huggingface.co Model
Url of mobileclip_s2_timm
mobileclip_s2_timm huggingface.co Url
Provider of mobileclip_s2_timm huggingface.co
Other API from apple
Updated:March 01 2025
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