File size: 1,335 Bytes
5945eb1
8b5f4f9
 
 
5945eb1
 
8b5f4f9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
478b527
5945eb1
8b5f4f9
 
 
2964102
8b5f4f9
478b527
19ac849
8b5f4f9
 
 
 
 
19ac849
57d6540
19ac849
2964102
a8aa362
19ac849
8b5f4f9
 
 
19ac849
8b5f4f9
19ac849
8b5f4f9
 
8933a1a
8b5f4f9
8933a1a
8b5f4f9
8933a1a
2964102
478b527
2964102
478b527
2964102
8933a1a
2964102
8933a1a
2964102
8933a1a
2964102
8933a1a
8b5f4f9
8933a1a
2964102
8933a1a
2964102
8933a1a
2964102
8933a1a
2964102
478b527
2964102
8933a1a
2964102
 
 
 
 
8b5f4f9
b566d9f
 
2964102
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
---
language: en
license: mit
library_name: timm
tags:
- image-classification
- resnet18
- cifar100
datasets: cifar100
metrics:
- accuracy
model-index:
- name: resnet18_cifar100
  results:
  - task:
      type: image-classification
    dataset:
      name: CIFAR-100
      type: cifar100
    metrics:
    - type: accuracy
      value: 0.7926
---

# Model Card for Model ID

This model is a small resnet18 trained on cifar100.

- **Test Accuracy:** 0.7926
- **License:** MIT

## How to Get Started with the Model

Use the code below to get started with the model.

```python
import detectors
import timm

model = timm.create_model("resnet18_cifar100", pretrained=True)
```

## Training Data

Training data is cifar100.

## Training Hyperparameters


- **config**: `scripts/train_configs/cifar100.json`

- **model**: `resnet18_cifar100`

- **dataset**: `cifar100`

- **batch_size**: `128`

- **epochs**: `300`

- **validation_frequency**: `5`

- **seed**: `1`

- **criterion**: `CrossEntropyLoss`

- **criterion_kwargs**: `{}`

- **optimizer**: `SGD`

- **lr**: `0.1`

- **optimizer_kwargs**: `{'momentum': 0.9, 'weight_decay': 0.0005}`

- **scheduler**: `CosineAnnealingLR`

- **scheduler_kwargs**: `{'T_max': 280}`

- **debug**: `False`


## Testing Data

Testing data is cifar100.

---

This model card was created by Eduardo Dadalto.