metadata

tags:
  - image-classification
  - timm
library_name: timm
license: other
license_name: apple-ascl
license_link: LICENSE

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.

Our smallest variant MobileCLIP-S0 obtains similar zero-shot performance as OpenAI's ViT-B/16 model while being 4.8x faster and 2.8x smaller.
MobileCLIP-S2 obtains 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