metadata

license: apache-2.0
base_model: microsoft/cvt-13
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: cvt-13-finetuned-flower
    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: 0.9368421052631579
          - name: Precision
            type: precision
            value: 0.9374630861809764
          - name: Recall
            type: recall
            value: 0.9368421052631579
          - name: F1
            type: f1
            value: 0.9341589949056075

cvt-13-finetuned-flower

This model is a fine-tuned version of microsoft/cvt-13 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.2151
Accuracy: 0.9368
Precision: 0.9375
Recall: 0.9368
F1: 0.9342

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: 0.005
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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.0555	1.0	40	0.3933	0.8766	0.8828	0.8766	0.8713
1.1941	2.0	80	1.0797	0.6726	0.7515	0.6726	0.6546
1.2286	3.0	120	0.8459	0.7347	0.7820	0.7347	0.7343
1.209	4.0	160	0.6660	0.7880	0.8173	0.7880	0.7833
1.1158	5.0	200	0.7348	0.7597	0.7809	0.7597	0.7561
1.1113	6.0	240	0.6387	0.8062	0.8164	0.8062	0.7986
1.0332	7.0	280	0.6555	0.7887	0.8064	0.7887	0.7831
1.0234	8.0	320	0.5776	0.8276	0.8447	0.8276	0.8177
0.9997	9.0	360	0.5784	0.8214	0.8421	0.8214	0.8169
0.9421	10.0	400	0.4667	0.8486	0.8600	0.8486	0.8453
0.9057	11.0	440	0.4508	0.8541	0.8711	0.8541	0.8487
0.8662	12.0	480	0.3517	0.8911	0.8938	0.8911	0.8868
0.8341	13.0	520	0.3191	0.8976	0.9021	0.8976	0.8945
0.757	14.0	560	0.2785	0.9183	0.9199	0.9183	0.9144
0.7906	15.0	600	0.2698	0.9201	0.9218	0.9201	0.9172
0.7464	16.0	640	0.2594	0.9216	0.9232	0.9216	0.9188
0.7335	17.0	680	0.2491	0.9263	0.9281	0.9263	0.9240
0.7085	18.0	720	0.2396	0.9303	0.9304	0.9303	0.9272
0.7177	19.0	760	0.2171	0.9350	0.9355	0.9350	0.9321
0.6735	20.0	800	0.2151	0.9368	0.9375	0.9368	0.9342

Framework versions

Transformers 4.39.3
Pytorch 2.0.1
Datasets 2.18.0
Tokenizers 0.15.2