metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: mit-b2-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: 0.8523956723338485
- task:
type: image-classification
name: Image Classification
dataset:
type: custom
name: custom
split: test
metrics:
- type: f1
value: 0.8580847578266328
name: F1
- type: precision
value: 0.8587893412503379
name: Precision
- type: recall
value: 0.8593508500772797
name: Recall
mit-b2-finetuned-memes
This model is a fine-tuned version of aaraki/vit-base-patch16-224-in21k-finetuned-cifar10 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.4137
- Accuracy: 0.8524
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.9727 | 0.99 | 40 | 0.8400 | 0.7334 |
| 0.5305 | 1.99 | 80 | 0.5147 | 0.8284 |
| 0.3124 | 2.99 | 120 | 0.4698 | 0.8145 |
| 0.2263 | 3.99 | 160 | 0.3892 | 0.8563 |
| 0.1453 | 4.99 | 200 | 0.3874 | 0.8570 |
| 0.1255 | 5.99 | 240 | 0.4097 | 0.8470 |
| 0.0989 | 6.99 | 280 | 0.3860 | 0.8570 |
| 0.0755 | 7.99 | 320 | 0.4141 | 0.8539 |
| 0.08 | 8.99 | 360 | 0.4049 | 0.8594 |
| 0.0639 | 9.99 | 400 | 0.4137 | 0.8524 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1