Hugging Face's logo

Amshaker
/
GroupMamba-Base

Model card Files
xet
Community
GroupMamba-Base
File size: 1,843 Bytes 
c5c868a
1b01b1a
c5c868a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
 
---
license: mit
---




<br>



# GroupMamba-Base Model Card



## Model Details



GroupMamba-Base is a generic backbone with 57M parameters trained on the ImageNet-1K dataset for vision tasks.



- **Model type:** Parameter-Efficient and Accurate Vision Backbone Based on Group Visual State Space Model
- **License:** Non-commercial license





### Model Sources



- **Repository:** https://github.com/amshaker/GroupMamba
- **Paper:** https://arxiv.org/abs/X.X



## Uses



The primary use of GroupMamba is research on vision tasks, e.g., classification, segmentation, detection, and instance segmentation, with an SSM-based backbone.
The primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence.



## How to Get Started with the Model



- You can replace the backbone for vision tasks with the proposed GroupMamba: https://github.com/Amshaker/GroupMamba/blob/main/classification/models/groupmamba.py
- Then, you can load this checkpoint and start fine-tuning.



## Training Details



GroupMamba is pretrained on ImageNet-1K with classification supervision.
The training data is around 1.3M images from [ImageNet-1K dataset](https://www.image-net.org/challenges/LSVRC/2012/).
See more details in this [paper](https://arxiv.org/abs/X.X.



## Evaluation



GroupMamba-Tiny is evaluated on ImageNet-1K val set, and achieves 84.5% Top-1 Acc with only 57M parameters. See more details in this [paper](https://arxiv.org/abs/X.X).



## Additional Information



### Citation Information



```
@article{GroupMamba,
  title={GroupMamba: Parameter-Efficient and Accurate Group Visual State Space Model},
  author={Abdelrahman Shaker and Syed Talal Wasim and Salman Khan and Gall Jürgen and Fahad Khan},
  journal={arXiv preprint arXiv:X.X},
  year={2024}
}
```