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: 0.0290
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: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.94	4	0.0290	1.0
No log	1.94	8	0.0290	1.0
No log	2.94	12	0.0290	1.0
No log	3.94	16	0.0290	1.0
0.2595	4.94	20	0.0290	1.0
0.2595	5.94	24	0.0290	1.0
0.2595	6.94	28	0.0290	1.0
0.2595	7.94	32	0.0290	1.0
0.2595	8.94	36	0.0290	1.0
0.2679	9.94	40	0.0290	1.0
0.2679	10.94	44	0.0290	1.0
0.2679	11.94	48	0.0290	1.0
0.2679	12.94	52	0.0290	1.0
0.2679	13.94	56	0.0290	1.0
0.275	14.94	60	0.0290	1.0
0.275	15.94	64	0.0290	1.0
0.275	16.94	68	0.0290	1.0
0.275	17.94	72	0.0290	1.0
0.275	18.94	76	0.0290	1.0
0.248	19.94	80	0.0290	1.0
0.248	20.94	84	0.0290	1.0
0.248	21.94	88	0.0290	1.0
0.248	22.94	92	0.0290	1.0
0.248	23.94	96	0.0290	1.0
0.2669	24.94	100	0.0290	1.0
0.2669	25.94	104	0.0290	1.0
0.2669	26.94	108	0.0290	1.0
0.2669	27.94	112	0.0290	1.0
0.2669	28.94	116	0.0290	1.0
0.2589	29.94	120	0.0290	1.0
0.2589	30.94	124	0.0290	1.0
0.2589	31.94	128	0.0290	1.0
0.2589	32.94	132	0.0290	1.0
0.2589	33.94	136	0.0290	1.0
0.278	34.94	140	0.0290	1.0
0.278	35.94	144	0.0290	1.0
0.278	36.94	148	0.0290	1.0
0.278	37.94	152	0.0290	1.0
0.278	38.94	156	0.0290	1.0
0.273	39.94	160	0.0290	1.0
0.273	40.94	164	0.0290	1.0
0.273	41.94	168	0.0290	1.0
0.273	42.94	172	0.0290	1.0
0.273	43.94	176	0.0290	1.0
0.2535	44.94	180	0.0290	1.0
0.2535	45.94	184	0.0290	1.0
0.2535	46.94	188	0.0290	1.0
0.2535	47.94	192	0.0290	1.0
0.2535	48.94	196	0.0290	1.0
0.2762	49.94	200	0.0290	1.0
0.2762	50.94	204	0.0290	1.0
0.2762	51.94	208	0.0290	1.0
0.2762	52.94	212	0.0290	1.0
0.2762	53.94	216	0.0290	1.0
0.2798	54.94	220	0.0290	1.0
0.2798	55.94	224	0.0290	1.0
0.2798	56.94	228	0.0290	1.0
0.2798	57.94	232	0.0290	1.0
0.2798	58.94	236	0.0290	1.0
0.2709	59.94	240	0.0290	1.0

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.8.0
Tokenizers 0.13.2