metadata

license: apache-2.0
base_model: Zetatech/pvt-tiny-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: pvt-tiny-224
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7833333333333333
          - name: Precision
            type: precision
            value: 0.7680555555555556
          - name: Recall
            type: recall
            value: 0.7833333333333333

pvt-tiny-224

This model is a fine-tuned version of Zetatech/pvt-tiny-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.4869
Accuracy: 0.7833
Precision: 0.7681
Recall: 0.7833
F1 Score: 0.7632

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: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1 Score
No log	1.0	4	0.5984	0.7333	0.5378	0.7333	0.6205
No log	2.0	8	0.6103	0.7333	0.5378	0.7333	0.6205
No log	3.0	12	0.5861	0.7333	0.5378	0.7333	0.6205
No log	4.0	16	0.5478	0.7333	0.5378	0.7333	0.6205
No log	5.0	20	0.5961	0.725	0.7119	0.725	0.7171
No log	6.0	24	0.5317	0.7542	0.7261	0.7542	0.7159
No log	7.0	28	0.5620	0.7458	0.7289	0.7458	0.7342
0.5878	8.0	32	0.5281	0.7542	0.7316	0.7542	0.6973
0.5878	9.0	36	0.5434	0.7625	0.7395	0.7625	0.7368
0.5878	10.0	40	0.5236	0.775	0.7658	0.775	0.7321
0.5878	11.0	44	0.5411	0.7542	0.7382	0.7542	0.7429
0.5878	12.0	48	0.5186	0.7708	0.7507	0.7708	0.7460
0.5878	13.0	52	0.5194	0.7667	0.7500	0.7667	0.7533
0.5878	14.0	56	0.5049	0.7875	0.7739	0.7875	0.7621
0.4973	15.0	60	0.5125	0.7833	0.7691	0.7833	0.7709
0.4973	16.0	64	0.5000	0.7917	0.7804	0.7917	0.7656
0.4973	17.0	68	0.5137	0.7583	0.7560	0.7583	0.7571
0.4973	18.0	72	0.4833	0.8	0.788	0.8	0.7833
0.4973	19.0	76	0.4929	0.7917	0.7816	0.7917	0.7843
0.4973	20.0	80	0.4858	0.8042	0.7930	0.8042	0.7887
0.4973	21.0	84	0.4900	0.7917	0.7777	0.7917	0.7743
0.4973	22.0	88	0.4886	0.7958	0.7829	0.7958	0.7815
0.439	23.0	92	0.4841	0.7917	0.7778	0.7917	0.7723
0.439	24.0	96	0.4855	0.8	0.7883	0.8	0.7885
0.439	25.0	100	0.4856	0.8	0.7879	0.8	0.7869
0.439	26.0	104	0.4839	0.8	0.7879	0.8	0.7869
0.439	27.0	108	0.4811	0.8	0.7879	0.8	0.7869
0.439	28.0	112	0.4834	0.8	0.7889	0.8	0.7901
0.439	29.0	116	0.4839	0.8	0.7889	0.8	0.7901
0.4092	30.0	120	0.4838	0.8	0.7889	0.8	0.7901

Framework versions

Transformers 4.33.3
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3