File size: 2,297 Bytes
c431c9d ccb0d48 c431c9d ccb0d48 c431c9d ccb0d48 c431c9d |
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 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-5e-4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: vuongnhathien/30VNFoods
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8682539682539683
---
<!-- 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. -->
# vit-base-5e-4
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the vuongnhathien/30VNFoods dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6221
- Accuracy: 0.8683
## 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: 0.0005
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7552 | 1.0 | 275 | 0.7489 | 0.7849 |
| 0.4161 | 2.0 | 550 | 0.6816 | 0.8127 |
| 0.2389 | 3.0 | 825 | 0.6486 | 0.8326 |
| 0.1523 | 4.0 | 1100 | 0.6459 | 0.8414 |
| 0.0917 | 5.0 | 1375 | 0.7039 | 0.8382 |
| 0.0492 | 6.0 | 1650 | 0.7023 | 0.8425 |
| 0.0175 | 7.0 | 1925 | 0.6089 | 0.8664 |
| 0.009 | 8.0 | 2200 | 0.5864 | 0.8775 |
| 0.0026 | 9.0 | 2475 | 0.5646 | 0.8783 |
| 0.0037 | 10.0 | 2750 | 0.5681 | 0.8803 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|