deit-base-patch16-224-FV-finetuned-memes

This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.6769
• Accuracy: 0.8485
• Precision: 0.8458
• Recall: 0.8485
• F1: 0.8464

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
1.2733	0.99	20	1.0893	0.5811	0.5790	0.5811	0.5293
0.7284	1.99	40	0.7351	0.7210	0.7642	0.7210	0.7271
0.4267	2.99	60	0.5202	0.7991	0.8104	0.7991	0.8033
0.2181	3.99	80	0.4605	0.8346	0.8351	0.8346	0.8334
0.1504	4.99	100	0.5281	0.8253	0.8281	0.8253	0.8266
0.1001	5.99	120	0.4945	0.8369	0.8336	0.8369	0.8347
0.0874	6.99	140	0.5902	0.8338	0.8370	0.8338	0.8348
0.0634	7.99	160	0.6088	0.8253	0.8221	0.8253	0.8234
0.0699	8.99	180	0.6210	0.8207	0.8202	0.8207	0.8186
0.0661	9.99	200	0.5675	0.8385	0.8417	0.8385	0.8393
0.0592	10.99	220	0.6550	0.8253	0.8324	0.8253	0.8275
0.0559	11.99	240	0.6400	0.8416	0.8370	0.8416	0.8387
0.0501	12.99	260	0.6726	0.8393	0.8353	0.8393	0.8350
0.0529	13.99	280	0.6285	0.8408	0.8399	0.8408	0.8401
0.0478	14.99	300	0.6423	0.8400	0.8380	0.8400	0.8384
0.0458	15.99	320	0.6632	0.8369	0.8337	0.8369	0.8348
0.048	16.99	340	0.6719	0.8423	0.8401	0.8423	0.8404
0.0417	17.99	360	0.6807	0.8423	0.8415	0.8423	0.8408
0.0461	18.99	380	0.6732	0.8454	0.8440	0.8454	0.8438
0.044	19.99	400	0.6769	0.8485	0.8458	0.8485	0.8464

Framework versions

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