README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: cvt-13-384-22k-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: 0.8315301391035549
          - name: Precision
            type: precision
            value: 0.8302128280229624
          - name: Recall
            type: recall
            value: 0.8315301391035549
          - name: F1
            type: f1
            value: 0.8292026505769348

cvt-13-384-22k-fv-finetuned-memes

This model is a fine-tuned version of microsoft/cvt-13-384-22k on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.5761
• Accuracy: 0.8315
• Precision: 0.8302
• Recall: 0.8315
• F1: 0.8292

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
1.3821	0.99	20	1.2780	0.4969	0.5083	0.4969	0.4458
1.0785	1.99	40	0.8633	0.6669	0.6658	0.6669	0.6500
0.8862	2.99	60	0.7110	0.7218	0.7258	0.7218	0.7013
0.665	3.99	80	0.5515	0.8045	0.8137	0.8045	0.8050
0.6056	4.99	100	0.5956	0.7960	0.8041	0.7960	0.7846
0.4779	5.99	120	0.6229	0.7937	0.7945	0.7937	0.7857
0.4554	6.99	140	0.5355	0.8099	0.8126	0.8099	0.8086
0.4249	7.99	160	0.5447	0.8269	0.8275	0.8269	0.8236
0.4313	8.99	180	0.5530	0.8153	0.8140	0.8153	0.8132
0.423	9.99	200	0.5346	0.8238	0.8230	0.8238	0.8223
0.3997	10.99	220	0.5413	0.8338	0.8347	0.8338	0.8338
0.4095	11.99	240	0.5999	0.8207	0.8231	0.8207	0.8177
0.3979	12.99	260	0.5632	0.8284	0.8255	0.8284	0.8250
0.3408	13.99	280	0.5725	0.8207	0.8198	0.8207	0.8196
0.3828	14.99	300	0.5631	0.8277	0.8258	0.8277	0.8260
0.3595	15.99	320	0.6005	0.8308	0.8297	0.8308	0.8275
0.3789	16.99	340	0.5840	0.8300	0.8271	0.8300	0.8273
0.3545	17.99	360	0.5983	0.8246	0.8226	0.8246	0.8222
0.3472	18.99	380	0.5795	0.8416	0.8382	0.8416	0.8390
0.355	19.99	400	0.5761	0.8315	0.8302	0.8315	0.8292

Framework versions

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