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: 0.9555555555555556

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: 0.2060
Accuracy: 0.9556

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	0.92	3	0.3269	0.9111
No log	1.92	6	0.2814	0.9333
No log	2.92	9	0.2625	0.9333
No log	3.92	12	0.2771	0.9333
No log	4.92	15	0.2419	0.9333
No log	5.92	18	0.2264	0.9111
0.3207	6.92	21	0.2530	0.9333
0.3207	7.92	24	0.2242	0.9333
0.3207	8.92	27	0.2060	0.9556
0.3207	9.92	30	0.1809	0.9556
0.3207	10.92	33	0.2070	0.9556
0.3207	11.92	36	0.1999	0.9556
0.3207	12.92	39	0.2013	0.9556
0.2066	13.92	42	0.2027	0.9556
0.2066	14.92	45	0.1809	0.9556
0.2066	15.92	48	0.1657	0.9556
0.2066	16.92	51	0.1728	0.9556
0.2066	17.92	54	0.2013	0.9556
0.2066	18.92	57	0.2226	0.9556
0.1894	19.92	60	0.2091	0.9556
0.1894	20.92	63	0.1940	0.9556
0.1894	21.92	66	0.1976	0.9556
0.1894	22.92	69	0.2232	0.9556
0.1894	23.92	72	0.2381	0.9556
0.1894	24.92	75	0.2405	0.9556
0.1894	25.92	78	0.2247	0.9556
0.1713	26.92	81	0.1895	0.9556
0.1713	27.92	84	0.1836	0.9556
0.1713	28.92	87	0.1985	0.9556
0.1713	29.92	90	0.2127	0.9556
0.1713	30.92	93	0.2098	0.9556
0.1713	31.92	96	0.2003	0.9556
0.1713	32.92	99	0.1849	0.9556
0.1428	33.92	102	0.1843	0.9556
0.1428	34.92	105	0.1900	0.9556
0.1428	35.92	108	0.1972	0.9556
0.1428	36.92	111	0.2023	0.9556
0.1428	37.92	114	0.2060	0.9556
0.1428	38.92	117	0.2093	0.9556
0.1443	39.92	120	0.2106	0.9556

Framework versions

Transformers 4.22.2
Pytorch 1.12.1+cu113
Datasets 2.5.2
Tokenizers 0.12.1