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--- |
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license: other |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: mit-b2-VF2-finetuned-memes |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8307573415765069 |
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- name: Precision |
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type: precision |
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value: 0.8272186656187493 |
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- name: Recall |
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type: recall |
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value: 0.8307573415765069 |
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- name: F1 |
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type: f1 |
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value: 0.8286939083150942 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mit-b2-VF2-finetuned-memes |
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This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6547 |
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- Accuracy: 0.8308 |
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- Precision: 0.8272 |
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- Recall: 0.8308 |
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- F1: 0.8287 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.00012 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.3077 | 0.99 | 20 | 1.1683 | 0.5549 | 0.5621 | 0.5549 | 0.5286 | |
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| 0.9359 | 1.99 | 40 | 0.8573 | 0.6731 | 0.6807 | 0.6731 | 0.6535 | |
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| 0.7219 | 2.99 | 60 | 0.7106 | 0.7272 | 0.7359 | 0.7272 | 0.7246 | |
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| 0.6013 | 3.99 | 80 | 0.6445 | 0.7550 | 0.7686 | 0.7550 | 0.7558 | |
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| 0.5243 | 4.99 | 100 | 0.6717 | 0.7573 | 0.8077 | 0.7573 | 0.7584 | |
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| 0.4409 | 5.99 | 120 | 0.5315 | 0.8068 | 0.8027 | 0.8068 | 0.7989 | |
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| 0.3325 | 6.99 | 140 | 0.5159 | 0.8230 | 0.8236 | 0.8230 | 0.8158 | |
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| 0.2719 | 7.99 | 160 | 0.5250 | 0.8215 | 0.8227 | 0.8215 | 0.8202 | |
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| 0.242 | 8.99 | 180 | 0.5087 | 0.8277 | 0.8260 | 0.8277 | 0.8268 | |
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| 0.2247 | 9.99 | 200 | 0.5313 | 0.8215 | 0.8275 | 0.8215 | 0.8218 | |
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| 0.1955 | 10.99 | 220 | 0.6167 | 0.8130 | 0.8062 | 0.8130 | 0.8073 | |
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| 0.1567 | 11.99 | 240 | 0.5859 | 0.8168 | 0.8185 | 0.8168 | 0.8173 | |
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| 0.1479 | 12.99 | 260 | 0.5938 | 0.8215 | 0.8169 | 0.8215 | 0.8178 | |
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| 0.1241 | 13.99 | 280 | 0.6187 | 0.8261 | 0.8234 | 0.8261 | 0.8239 | |
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| 0.1114 | 14.99 | 300 | 0.6419 | 0.8261 | 0.8351 | 0.8261 | 0.8293 | |
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| 0.1022 | 15.99 | 320 | 0.6322 | 0.8323 | 0.8284 | 0.8323 | 0.8294 | |
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| 0.0941 | 16.99 | 340 | 0.6595 | 0.8269 | 0.8266 | 0.8269 | 0.8263 | |
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| 0.0935 | 17.99 | 360 | 0.6674 | 0.8269 | 0.8218 | 0.8269 | 0.8237 | |
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| 0.089 | 18.99 | 380 | 0.6533 | 0.8253 | 0.8222 | 0.8253 | 0.8235 | |
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| 0.0794 | 19.99 | 400 | 0.6547 | 0.8308 | 0.8272 | 0.8308 | 0.8287 | |
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### Framework versions |
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- Transformers 4.24.0.dev0 |
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- Pytorch 1.11.0+cu102 |
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- Datasets 2.6.1.dev0 |
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- Tokenizers 0.13.1 |
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