Hugging Face's logo

timm
/
mobilenetv4_conv_large.e500_r256_in1k

Image Classification
timm
PyTorch
Safetensors
Model card Files Community
mobilenetv4_conv_large.e500_r256_in1k
File size: 4,940 Bytes 
8ad827d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
 
---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for mobilenetv4_conv_large.e500_r256_in1k

A MobileNet-V4 image classification model. Trained on ImageNet-1k by Ross Wightman.

Trained with `timm` scripts using hyper-parameters (mostly) similar to those in the paper.

NOTE: So far, these are the only known MNV4 weights. Official weights for Tensorflow models are unreleased.


## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
  - Params (M): 32.6
  - GMACs: 2.9
  - Activations (M): 12.1
  - Image size: train = 256 x 256, test = 320 x 320
- **Dataset:** ImageNet-1k
- **Papers:**
  - MobileNetV4 -- Universal Models for the Mobile Ecosystem: https://arxiv.org/abs/2404.10518
- **Original:** https://github.com/tensorflow/models/tree/master/official/vision

## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model('mobilenetv4_conv_large.e500_r256_in1k', pretrained=True)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```

### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'mobilenetv4_conv_large.e500_r256_in1k',
    pretrained=True,
    features_only=True,
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

for o in output:
    # print shape of each feature map in output
    # e.g.:
    #  torch.Size([1, 24, 128, 128])
    #  torch.Size([1, 48, 64, 64])
    #  torch.Size([1, 96, 32, 32])
    #  torch.Size([1, 192, 16, 16])
    #  torch.Size([1, 960, 8, 8])

    print(o.shape)
```

### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'mobilenetv4_conv_large.e500_r256_in1k',
    pretrained=True,
    num_classes=0,  # remove classifier nn.Linear
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor

# or equivalently (without needing to set num_classes=0)

output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 960, 8, 8) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```

## Model Comparison
### By Top-1

|model                                      |top1  |top1_err|top5  |top5_err|param_count|img_size|
|-------------------------------------------|------|--------|------|--------|-----------|--------|
|mobilenetv4_conv_large.e500_r256_in1k      |82.674|17.326  |96.31 |3.69    |32.59      |320     |
|mobilenetv4_conv_large.e500_r256_in1k      |81.862|18.138  |95.69 |4.31    |32.59      |256     |
|mobilenetv4_hybrid_medium.e500_r224_in1k   |81.276|18.724  |95.742|4.258   |11.07      |256     |
|mobilenetv4_conv_medium.e500_r256_in1k     |80.858|19.142  |95.768|4.232   |9.72       |320     |
|mobilenetv4_hybrid_medium.e500_r224_in1k   |80.442|19.558  |95.38 |4.62    |11.07      |224     |
|mobilenetv4_conv_blur_medium.e500_r224_in1k|80.142|19.858  |95.298|4.702   |9.72       |256     |
|mobilenetv4_conv_medium.e500_r256_in1k     |79.928|20.072  |95.184|4.816   |9.72       |256     |
|mobilenetv4_conv_medium.e500_r224_in1k     |79.808|20.192  |95.186|4.814   |9.72       |256     |
|mobilenetv4_conv_blur_medium.e500_r224_in1k|79.438|20.562  |94.932|5.068   |9.72       |224     |
|mobilenetv4_conv_medium.e500_r224_in1k     |79.094|20.906  |94.77 |5.23    |9.72       |224     |
|mobilenetv4_conv_small.e1200_r224_in1k     |74.292|25.708  |92.116|7.884   |3.77       |256     |
|mobilenetv4_conv_small.e1200_r224_in1k     |73.454|26.546  |91.34 |8.66    |3.77       |224     |