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: 0.9814814814814815

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: 0.1169
Accuracy: 0.9815

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	0.8	3	0.1169	0.9815
No log	1.8	6	0.1169	0.9815
No log	2.8	9	0.1169	0.9815
No log	3.8	12	0.1169	0.9815
No log	4.8	15	0.1169	0.9815
No log	5.8	18	0.1169	0.9815
0.3448	6.8	21	0.1169	0.9815
0.3448	7.8	24	0.1169	0.9815
0.3448	8.8	27	0.1169	0.9815
0.3448	9.8	30	0.1169	0.9815
0.3448	10.8	33	0.1169	0.9815
0.3448	11.8	36	0.1169	0.9815
0.3448	12.8	39	0.1169	0.9815
0.3344	13.8	42	0.1169	0.9815
0.3344	14.8	45	0.1169	0.9815
0.3344	15.8	48	0.1169	0.9815
0.3344	16.8	51	0.1169	0.9815
0.3344	17.8	54	0.1169	0.9815
0.3344	18.8	57	0.1169	0.9815
0.3274	19.8	60	0.1169	0.9815
0.3274	20.8	63	0.1169	0.9815
0.3274	21.8	66	0.1169	0.9815
0.3274	22.8	69	0.1169	0.9815
0.3274	23.8	72	0.1169	0.9815
0.3274	24.8	75	0.1169	0.9815
0.3274	25.8	78	0.1169	0.9815
0.3426	26.8	81	0.1169	0.9815
0.3426	27.8	84	0.1169	0.9815
0.3426	28.8	87	0.1169	0.9815
0.3426	29.8	90	0.1169	0.9815
0.3426	30.8	93	0.1169	0.9815
0.3426	31.8	96	0.1169	0.9815
0.3426	32.8	99	0.1169	0.9815
0.3436	33.8	102	0.1169	0.9815
0.3436	34.8	105	0.1169	0.9815
0.3436	35.8	108	0.1169	0.9815
0.3436	36.8	111	0.1169	0.9815
0.3436	37.8	114	0.1169	0.9815
0.3436	38.8	117	0.1169	0.9815
0.3243	39.8	120	0.1169	0.9815

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2