README.md

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: 0.9733333333333334

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: 0.1787
• Accuracy: 0.9733

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: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.91	5	0.1787	0.9733
No log	1.91	10	0.1787	0.9733
No log	2.91	15	0.1787	0.9733
0.3799	3.91	20	0.1787	0.9733
0.3799	4.91	25	0.1787	0.9733
0.3799	5.91	30	0.1787	0.9733
0.3799	6.91	35	0.1787	0.9733
0.3648	7.91	40	0.1787	0.9733
0.3648	8.91	45	0.1787	0.9733
0.3648	9.91	50	0.1787	0.9733
0.3648	10.91	55	0.1787	0.9733
0.3954	11.91	60	0.1787	0.9733
0.3954	12.91	65	0.1787	0.9733
0.3954	13.91	70	0.1787	0.9733
0.3954	14.91	75	0.1787	0.9733
0.3926	15.91	80	0.1787	0.9733
0.3926	16.91	85	0.1787	0.9733
0.3926	17.91	90	0.1787	0.9733
0.3926	18.91	95	0.1787	0.9733
0.3801	19.91	100	0.1787	0.9733
0.3801	20.91	105	0.1787	0.9733
0.3801	21.91	110	0.1787	0.9733
0.3801	22.91	115	0.1787	0.9733
0.3815	23.91	120	0.1787	0.9733
0.3815	24.91	125	0.1787	0.9733
0.3815	25.91	130	0.1787	0.9733
0.3815	26.91	135	0.1787	0.9733
0.3955	27.91	140	0.1787	0.9733
0.3955	28.91	145	0.1787	0.9733
0.3955	29.91	150	0.1787	0.9733
0.3955	30.91	155	0.1787	0.9733
0.3854	31.91	160	0.1787	0.9733
0.3854	32.91	165	0.1787	0.9733
0.3854	33.91	170	0.1787	0.9733
0.3854	34.91	175	0.1787	0.9733
0.3949	35.91	180	0.1787	0.9733
0.3949	36.91	185	0.1787	0.9733
0.3949	37.91	190	0.1787	0.9733
0.3949	38.91	195	0.1787	0.9733
0.423	39.91	200	0.1787	0.9733
0.423	40.91	205	0.1787	0.9733
0.423	41.91	210	0.1787	0.9733
0.423	42.91	215	0.1787	0.9733
0.3761	43.91	220	0.1787	0.9733
0.3761	44.91	225	0.1787	0.9733
0.3761	45.91	230	0.1787	0.9733
0.3761	46.91	235	0.1787	0.9733
0.3673	47.91	240	0.1787	0.9733
0.3673	48.91	245	0.1787	0.9733
0.3673	49.91	250	0.1787	0.9733
0.3673	50.91	255	0.1787	0.9733
0.3639	51.91	260	0.1787	0.9733
0.3639	52.91	265	0.1787	0.9733
0.3639	53.91	270	0.1787	0.9733
0.3639	54.91	275	0.1787	0.9733
0.4031	55.91	280	0.1787	0.9733
0.4031	56.91	285	0.1787	0.9733
0.4031	57.91	290	0.1787	0.9733
0.4031	58.91	295	0.1787	0.9733
0.3787	59.91	300	0.1787	0.9733

Framework versions

• Transformers 4.26.0
• Pytorch 1.13.1+cu116
• Datasets 2.9.0
• Tokenizers 0.13.2