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: 0.9767441860465116

delivery_truck_classification

This model is a fine-tuned version of JEdward7777/delivery_truck_classification on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.1403
Accuracy: 0.9767

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	1.0	3	0.1491	0.9535
No log	2.0	6	0.1462	0.9535
No log	3.0	9	0.1403	0.9767
No log	4.0	12	0.1431	0.9767
No log	5.0	15	0.1761	0.9535
No log	6.0	18	0.1930	0.9535
0.2637	7.0	21	0.1677	0.9535
0.2637	8.0	24	0.1835	0.9767
0.2637	9.0	27	0.1804	0.9535
0.2637	10.0	30	0.1856	0.9535
0.2637	11.0	33	0.1719	0.9535
0.2637	12.0	36	0.1680	0.9535
0.2637	13.0	39	0.1571	0.9535
0.1687	14.0	42	0.1333	0.9535
0.1687	15.0	45	0.1285	0.9535
0.1687	16.0	48	0.1293	0.9535
0.1687	17.0	51	0.1208	0.9767
0.1687	18.0	54	0.1061	0.9767
0.1687	19.0	57	0.0978	0.9767
0.1435	20.0	60	0.1100	0.9535
0.1435	21.0	63	0.1205	0.9535
0.1435	22.0	66	0.1027	0.9767
0.1435	23.0	69	0.1041	0.9767
0.1435	24.0	72	0.1021	0.9767
0.1435	25.0	75	0.0974	0.9767
0.1435	26.0	78	0.1006	0.9535
0.1361	27.0	81	0.1011	0.9535
0.1361	28.0	84	0.0993	0.9767
0.1361	29.0	87	0.0951	0.9767
0.1361	30.0	90	0.0971	0.9767
0.1361	31.0	93	0.1036	0.9767
0.1361	32.0	96	0.1085	0.9767
0.1361	33.0	99	0.1099	0.9767
0.1221	34.0	102	0.1115	0.9767
0.1221	35.0	105	0.1133	0.9767
0.1221	36.0	108	0.1184	0.9535
0.1221	37.0	111	0.1215	0.9535
0.1221	38.0	114	0.1224	0.9535
0.1221	39.0	117	0.1222	0.9535
0.1135	40.0	120	0.1217	0.9535

Framework versions

Transformers 4.22.2
Pytorch 1.12.1+cu113
Datasets 2.5.1
Tokenizers 0.12.1