metadata

license: other
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: mit-b2-fv-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: 0.8323029366306027
          - name: Precision
            type: precision
            value: 0.831217385971583
          - name: Recall
            type: recall
            value: 0.8323029366306027
          - name: F1
            type: f1
            value: 0.831492653119617

mit-b2-fv-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: 0.5984
Accuracy: 0.8323
Precision: 0.8312
Recall: 0.8323
F1: 0.8315

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
1.3683	0.99	20	1.1798	0.5703	0.4914	0.5703	0.4915
1.0113	1.99	40	1.0384	0.6159	0.6813	0.6159	0.6274
0.7581	2.99	60	0.8348	0.6808	0.7377	0.6808	0.6840
0.6241	3.99	80	0.6034	0.7713	0.7864	0.7713	0.7735
0.4999	4.99	100	0.5481	0.7944	0.8000	0.7944	0.7909
0.3981	5.99	120	0.5253	0.8022	0.8091	0.8022	0.8000
0.3484	6.99	140	0.4688	0.8238	0.8147	0.8238	0.8146
0.3142	7.99	160	0.6245	0.7867	0.8209	0.7867	0.7920
0.2339	8.99	180	0.5053	0.8362	0.8426	0.8362	0.8355
0.2284	9.99	200	0.5070	0.8230	0.8220	0.8230	0.8187
0.1824	10.99	220	0.5780	0.8006	0.8138	0.8006	0.8035
0.1561	11.99	240	0.5429	0.8253	0.8197	0.8253	0.8218
0.1229	12.99	260	0.5325	0.8331	0.8296	0.8331	0.8303
0.1232	13.99	280	0.5595	0.8277	0.8290	0.8277	0.8273
0.118	14.99	300	0.5974	0.8292	0.8345	0.8292	0.8299
0.11	15.99	320	0.5796	0.8253	0.8228	0.8253	0.8231
0.0948	16.99	340	0.5581	0.8346	0.8358	0.8346	0.8349
0.0985	17.99	360	0.5700	0.8338	0.8301	0.8338	0.8318
0.0821	18.99	380	0.5756	0.8331	0.8343	0.8331	0.8335
0.0813	19.99	400	0.5984	0.8323	0.8312	0.8323	0.8315

Framework versions

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