metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: resnet-50-FV2-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: 0.6452859350850078
          - name: Precision
            type: precision
            value: 0.5727919568038408
          - name: Recall
            type: recall
            value: 0.6452859350850078
          - name: F1
            type: f1
            value: 0.5963647629954705

resnet-50-FV2-finetuned-memes

This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.9263
Accuracy: 0.6453
Precision: 0.5728
Recall: 0.6453
F1: 0.5964

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
1.5763	0.99	20	1.5575	0.4281	0.2966	0.4281	0.2669
1.4761	1.99	40	1.4424	0.4343	0.1886	0.4343	0.2630
1.3563	2.99	60	1.3240	0.4343	0.1886	0.4343	0.2630
1.2824	3.99	80	1.2636	0.4389	0.3097	0.4389	0.2734
1.2315	4.99	100	1.2119	0.4529	0.3236	0.4529	0.3042
1.1956	5.99	120	1.1764	0.4900	0.3731	0.4900	0.3692
1.1452	6.99	140	1.1424	0.5147	0.3963	0.5147	0.4090
1.1076	7.99	160	1.1190	0.5371	0.4121	0.5371	0.4392
1.0679	8.99	180	1.0825	0.5719	0.4465	0.5719	0.4831
1.0432	9.99	200	1.0482	0.5750	0.5404	0.5750	0.4930
0.9903	10.99	220	1.0275	0.5958	0.5459	0.5958	0.5241
0.9675	11.99	240	1.0145	0.6051	0.5350	0.6051	0.5379
0.9335	12.99	260	0.9860	0.6175	0.5537	0.6175	0.5527
0.9157	13.99	280	0.9683	0.6105	0.5386	0.6105	0.5504
0.8901	14.99	300	0.9558	0.6352	0.5686	0.6352	0.5833
0.8722	15.99	320	0.9382	0.6345	0.5657	0.6345	0.5807
0.854	16.99	340	0.9322	0.6376	0.5623	0.6376	0.5856
0.8494	17.99	360	0.9287	0.6422	0.6675	0.6422	0.5918
0.8652	18.99	380	0.9212	0.6399	0.5640	0.6399	0.5863
0.846	19.99	400	0.9263	0.6453	0.5728	0.6453	0.5964

Framework versions

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