File size: 2,877 Bytes
46d882e da03fe3 46d882e ebf8d3a 46d882e ebf8d3a c2d1640 ebf8d3a c2d1640 46d882e c2d1640 ebf8d3a c2d1640 46d882e 2fe4aa0 412612a 46d882e da03fe3 c2d1640 46d882e 412612a |
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 |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- renovation
metrics:
- accuracy
model-index:
- name: vit-base-renovation
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: renovation
type: renovation
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6863636363636364
---
<!-- 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-renovation
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co./google/vit-base-patch16-224-in21k) on the renovation dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9761
- Accuracy: 0.6864
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9737 | 0.2 | 25 | 1.0076 | 0.5045 |
| 0.862 | 0.4 | 50 | 1.0220 | 0.5045 |
| 0.9064 | 0.6 | 75 | 0.9076 | 0.5591 |
| 0.8528 | 0.81 | 100 | 0.8157 | 0.65 |
| 0.8848 | 1.01 | 125 | 0.8089 | 0.6273 |
| 0.6608 | 1.21 | 150 | 0.8615 | 0.6409 |
| 0.6748 | 1.41 | 175 | 0.8426 | 0.6318 |
| 0.6559 | 1.61 | 200 | 0.8427 | 0.6091 |
| 0.5654 | 1.81 | 225 | 0.8267 | 0.6682 |
| 0.5254 | 2.02 | 250 | 0.7622 | 0.6545 |
| 0.2778 | 2.22 | 275 | 0.9481 | 0.6636 |
| 0.309 | 2.42 | 300 | 0.8998 | 0.6409 |
| 0.2396 | 2.62 | 325 | 0.9171 | 0.6409 |
| 0.2773 | 2.82 | 350 | 1.0582 | 0.6091 |
| 0.2516 | 3.02 | 375 | 0.9362 | 0.6455 |
| 0.1578 | 3.23 | 400 | 0.9264 | 0.6773 |
| 0.0979 | 3.43 | 425 | 0.9470 | 0.6773 |
| 0.0836 | 3.63 | 450 | 0.9941 | 0.6682 |
| 0.126 | 3.83 | 475 | 0.9761 | 0.6864 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|