Image Classification
timm
PyTorch
reach-vb HF staff commited on
Commit
fa5f14a
1 Parent(s): 57ed689

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +28 -2
README.md CHANGED
@@ -3,6 +3,32 @@ tags:
3
  - image-classification
4
  - timm
5
  library_name: timm
6
- license: apache-2.0
 
 
7
  ---
8
- # Model card for vit_base_mci_224.apple_mclip_lt
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3
  - image-classification
4
  - timm
5
  library_name: timm
6
+ license: other
7
+ license_name: apple-ascl
8
+ license_link: LICENSE
9
  ---
10
+
11
+ # MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training
12
+
13
+ MobileCLIP was introduced in [MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training
14
+ ](https://arxiv.org/pdf/2311.17049.pdf) (CVPR 2024), by Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel.
15
+
16
+ This repository contains the **MobileCLIP-B (LT)** checkpoint for timm.
17
+
18
+ ![MobileCLIP Performance Figure](fig_accuracy_latency.png)
19
+
20
+ ### Highlights
21
+
22
+ * Our smallest variant `MobileCLIP-S0` obtains similar zero-shot performance as [OpenAI](https://arxiv.org/abs/2103.00020)'s ViT-B/16 model while being 4.8x faster and 2.8x smaller.
23
+ * `MobileCLIP-S2` obtains better avg zero-shot performance than [SigLIP](https://arxiv.org/abs/2303.15343)'s ViT-B/16 model while being 2.3x faster and 2.1x smaller, and trained with 3x less seen samples.
24
+ * `MobileCLIP-B`(LT) attains zero-shot ImageNet performance of **77.2%** which is significantly better than recent works like [DFN](https://arxiv.org/abs/2309.17425) and [SigLIP](https://arxiv.org/abs/2303.15343) with similar architectures or even [OpenAI's ViT-L/14@336](https://arxiv.org/abs/2103.00020).
25
+
26
+ ## Checkpoints
27
+
28
+ | Model | # Seen <BR>Samples (B) | # Params (M) <BR> (img + txt) | Latency (ms) <BR> (img + txt) | IN-1k Zero-Shot <BR> Top-1 Acc. (%) | Avg. Perf. (%) <BR> on 38 datasets |
29
+ |:----------------------------------------------------------|:----------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------------:|:----------------------------------:|
30
+ | [MobileCLIP-S0](https://hf.co/pcuenq/MobileCLIP-S0) | 13 | 11.4 + 42.4 | 1.5 + 1.6 | 67.8 | 58.1 |
31
+ | [MobileCLIP-S1](https://hf.co/pcuenq/MobileCLIP-S1) | 13 | 21.5 + 63.4 | 2.5 + 3.3 | 72.6 | 61.3 |
32
+ | [MobileCLIP-S2](https://hf.co/pcuenq/MobileCLIP-S2) | 13 | 35.7 + 63.4 | 3.6 + 3.3 | 74.4 | 63.7 |
33
+ | [MobileCLIP-B](https://hf.co/pcuenq/MobileCLIP-B) | 13 | 86.3 + 63.4 | 10.4 + 3.3 | 76.8 | 65.2 |
34
+ | [MobileCLIP-B (LT)](https://hf.co/pcuenq/MobileCLIP-B-LT) | 36 | 86.3 + 63.4 | 10.4 + 3.3 | 77.2 | 65.8 |