Hugging Face's logo

100rab25
/
spa_images_classifier_jd_v1_convnext

Image Classification
Transformers
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
spa_images_classifier_jd_v1_convnext
File size: 2,425 Bytes 
38c2ef4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
74ecf1e
38c2ef4
 
 
 
 
 
 
 
 
74ecf1e
 
38c2ef4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: spa_images_classifier_jd_v1_convnext
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.978066110596231
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# spa_images_classifier_jd_v1_convnext

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0662
- Accuracy: 0.9781

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2494        | 1.0   | 227  | 0.1194          | 0.9555   |
| 0.2333        | 2.0   | 455  | 0.1008          | 0.9635   |
| 0.1977        | 3.0   | 683  | 0.0855          | 0.9703   |
| 0.1405        | 4.0   | 911  | 0.0792          | 0.9744   |
| 0.1575        | 5.0   | 1138 | 0.0734          | 0.9731   |
| 0.0948        | 6.0   | 1366 | 0.0666          | 0.9778   |
| 0.1049        | 7.0   | 1594 | 0.0662          | 0.9781   |
| 0.0928        | 8.0   | 1822 | 0.0693          | 0.9774   |
| 0.0903        | 9.0   | 2049 | 0.0704          | 0.9771   |
| 0.0759        | 9.97  | 2270 | 0.0652          | 0.9778   |


### Framework versions

- Transformers 4.35.0
- Pytorch 1.12.1+cu113
- Datasets 2.17.1
- Tokenizers 0.14.1