metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: delivery_truck_classification
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: 1
delivery_truck_classification
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0942
- Accuracy: 1.0
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: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.67 | 1 | 1.8688 | 0.1818 |
| No log | 1.67 | 2 | 1.7920 | 0.1818 |
| No log | 2.67 | 3 | 1.6533 | 0.3636 |
| No log | 3.67 | 4 | 1.4775 | 0.4545 |
| No log | 4.67 | 5 | 1.2912 | 0.5909 |
| No log | 5.67 | 6 | 1.1475 | 0.7273 |
| No log | 6.67 | 7 | 1.0266 | 0.7727 |
| No log | 7.67 | 8 | 0.9196 | 0.7727 |
| No log | 8.67 | 9 | 0.8273 | 0.8182 |
| No log | 9.67 | 10 | 0.7492 | 0.8182 |
| No log | 10.67 | 11 | 0.6857 | 0.9091 |
| No log | 11.67 | 12 | 0.6369 | 0.9091 |
| No log | 12.67 | 13 | 0.5916 | 1.0 |
| No log | 13.67 | 14 | 0.5462 | 1.0 |
| No log | 14.67 | 15 | 0.4927 | 1.0 |
| No log | 15.67 | 16 | 0.4390 | 1.0 |
| No log | 16.67 | 17 | 0.3914 | 1.0 |
| No log | 17.67 | 18 | 0.3446 | 1.0 |
| No log | 18.67 | 19 | 0.3019 | 1.0 |
| 1.7058 | 19.67 | 20 | 0.2611 | 1.0 |
| 1.7058 | 20.67 | 21 | 0.2289 | 1.0 |
| 1.7058 | 21.67 | 22 | 0.1960 | 1.0 |
| 1.7058 | 22.67 | 23 | 0.1711 | 1.0 |
| 1.7058 | 23.67 | 24 | 0.1568 | 1.0 |
| 1.7058 | 24.67 | 25 | 0.1463 | 1.0 |
| 1.7058 | 25.67 | 26 | 0.1383 | 1.0 |
| 1.7058 | 26.67 | 27 | 0.1323 | 1.0 |
| 1.7058 | 27.67 | 28 | 0.1268 | 1.0 |
| 1.7058 | 28.67 | 29 | 0.1199 | 1.0 |
| 1.7058 | 29.67 | 30 | 0.1145 | 1.0 |
| 1.7058 | 30.67 | 31 | 0.1129 | 1.0 |
| 1.7058 | 31.67 | 32 | 0.1095 | 1.0 |
| 1.7058 | 32.67 | 33 | 0.1079 | 1.0 |
| 1.7058 | 33.67 | 34 | 0.1053 | 1.0 |
| 1.7058 | 34.67 | 35 | 0.1034 | 1.0 |
| 1.7058 | 35.67 | 36 | 0.0990 | 1.0 |
| 1.7058 | 36.67 | 37 | 0.0963 | 1.0 |
| 1.7058 | 37.67 | 38 | 0.0952 | 1.0 |
| 1.7058 | 38.67 | 39 | 0.0944 | 1.0 |
| 0.6083 | 39.67 | 40 | 0.0942 | 1.0 |
Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1