File size: 3,525 Bytes
2d36e29 de7fd94 3a21d2e 2d36e29 a7a741b 2d36e29 add97f9 4c53a06 add97f9 2d36e29 3a21d2e 2d36e29 3a21d2e 2d36e29 361b516 2d36e29 |
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 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fashion-clothing-decade
results: []
---
<!-- 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. -->
# Fashion Clothing Decade
This model predicts what decade clothing is from. It takes an image and outputs one of the following labels:
**1910s, 1920s, 1930s, 1940s, 1950s, 1960s, 1970s, 1980s, 1990s, 2000s**
### How to use
```python
from transformers import pipeline
pipe = pipeline("image-classification", model="tonyassi/fashion-clothing-decade")
result = pipe('image.png')
print(result)
```
## Dataset
Trained on a total of 2500 images. ~250 images from each label.
### 1910s
### 1920s
### 1930s
### 1940s
### 1950s
### 1960s
### 1970s
### 1980s
### 1990s
### 2000s
## Model description
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k).
## Training and evaluation data
- Loss: 0.8707
- Accuracy: 0.7505
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|