metadata

license: other
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: mit-b2-VF2-finetuned-memes
    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: 0.8307573415765069
          - name: Precision
            type: precision
            value: 0.8272186656187493
          - name: Recall
            type: recall
            value: 0.8307573415765069
          - name: F1
            type: f1
            value: 0.8286939083150942

mit-b2-VF2-finetuned-memes

This model is a fine-tuned version of nvidia/mit-b2 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.6547
Accuracy: 0.8308
Precision: 0.8272
Recall: 0.8308
F1: 0.8287

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: 0.00012
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
1.3077	0.99	20	1.1683	0.5549	0.5621	0.5549	0.5286
0.9359	1.99	40	0.8573	0.6731	0.6807	0.6731	0.6535
0.7219	2.99	60	0.7106	0.7272	0.7359	0.7272	0.7246
0.6013	3.99	80	0.6445	0.7550	0.7686	0.7550	0.7558
0.5243	4.99	100	0.6717	0.7573	0.8077	0.7573	0.7584
0.4409	5.99	120	0.5315	0.8068	0.8027	0.8068	0.7989
0.3325	6.99	140	0.5159	0.8230	0.8236	0.8230	0.8158
0.2719	7.99	160	0.5250	0.8215	0.8227	0.8215	0.8202
0.242	8.99	180	0.5087	0.8277	0.8260	0.8277	0.8268
0.2247	9.99	200	0.5313	0.8215	0.8275	0.8215	0.8218
0.1955	10.99	220	0.6167	0.8130	0.8062	0.8130	0.8073
0.1567	11.99	240	0.5859	0.8168	0.8185	0.8168	0.8173
0.1479	12.99	260	0.5938	0.8215	0.8169	0.8215	0.8178
0.1241	13.99	280	0.6187	0.8261	0.8234	0.8261	0.8239
0.1114	14.99	300	0.6419	0.8261	0.8351	0.8261	0.8293
0.1022	15.99	320	0.6322	0.8323	0.8284	0.8323	0.8294
0.0941	16.99	340	0.6595	0.8269	0.8266	0.8269	0.8263
0.0935	17.99	360	0.6674	0.8269	0.8218	0.8269	0.8237
0.089	18.99	380	0.6533	0.8253	0.8222	0.8253	0.8235
0.0794	19.99	400	0.6547	0.8308	0.8272	0.8308	0.8287

Framework versions

Transformers 4.24.0.dev0
Pytorch 1.11.0+cu102
Datasets 2.6.1.dev0
Tokenizers 0.13.1