metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: resnet152-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: 0.7557959814528593
          - name: Precision
            type: precision
            value: 0.7556690736625777
          - name: Recall
            type: recall
            value: 0.7557959814528593
          - name: F1
            type: f1
            value: 0.7545674798253312

resnet152-FV-finetuned-memes

This model is a fine-tuned version of microsoft/resnet-152 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.6772
Accuracy: 0.7558
Precision: 0.7557
Recall: 0.7558
F1: 0.7546

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
1.5739	0.99	20	1.5427	0.4521	0.3131	0.4521	0.2880
1.4353	1.99	40	1.3786	0.4490	0.3850	0.4490	0.2791
1.3026	2.99	60	1.2734	0.4799	0.3073	0.4799	0.3393
1.1579	3.99	80	1.1378	0.5278	0.4300	0.5278	0.4143
1.0276	4.99	100	1.0231	0.5734	0.4497	0.5734	0.4865
0.8826	5.99	120	0.9228	0.6252	0.5983	0.6252	0.5637
0.766	6.99	140	0.8441	0.6662	0.6474	0.6662	0.6320
0.6732	7.99	160	0.8009	0.6901	0.6759	0.6901	0.6704
0.5653	8.99	180	0.7535	0.7218	0.7141	0.7218	0.7129
0.4957	9.99	200	0.7317	0.7257	0.7248	0.7257	0.7200
0.4534	10.99	220	0.6808	0.7434	0.7405	0.7434	0.7390
0.3792	11.99	240	0.6949	0.7450	0.7454	0.7450	0.7399
0.3489	12.99	260	0.6746	0.7496	0.7511	0.7496	0.7474
0.3113	13.99	280	0.6637	0.7573	0.7638	0.7573	0.7579
0.2947	14.99	300	0.6451	0.7589	0.7667	0.7589	0.7610
0.2776	15.99	320	0.6754	0.7543	0.7565	0.7543	0.7525
0.2611	16.99	340	0.6808	0.7550	0.7607	0.7550	0.7529
0.2428	17.99	360	0.7005	0.7457	0.7497	0.7457	0.7404
0.2346	18.99	380	0.6597	0.7573	0.7642	0.7573	0.7590
0.2367	19.99	400	0.6772	0.7558	0.7557	0.7558	0.7546

Framework versions

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