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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-fv-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: 0.8323029366306027 |
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- name: Precision |
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type: precision |
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value: 0.831217385971583 |
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- name: Recall |
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type: recall |
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value: 0.8323029366306027 |
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- name: F1 |
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type: f1 |
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value: 0.831492653119617 |
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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-fv-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: 0.5984 |
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- Accuracy: 0.8323 |
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- Precision: 0.8312 |
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- Recall: 0.8323 |
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- F1: 0.8315 |
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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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| 1.3683 | 0.99 | 20 | 1.1798 | 0.5703 | 0.4914 | 0.5703 | 0.4915 | |
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| 1.0113 | 1.99 | 40 | 1.0384 | 0.6159 | 0.6813 | 0.6159 | 0.6274 | |
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| 0.7581 | 2.99 | 60 | 0.8348 | 0.6808 | 0.7377 | 0.6808 | 0.6840 | |
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| 0.6241 | 3.99 | 80 | 0.6034 | 0.7713 | 0.7864 | 0.7713 | 0.7735 | |
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| 0.4999 | 4.99 | 100 | 0.5481 | 0.7944 | 0.8000 | 0.7944 | 0.7909 | |
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| 0.3981 | 5.99 | 120 | 0.5253 | 0.8022 | 0.8091 | 0.8022 | 0.8000 | |
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| 0.3484 | 6.99 | 140 | 0.4688 | 0.8238 | 0.8147 | 0.8238 | 0.8146 | |
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| 0.3142 | 7.99 | 160 | 0.6245 | 0.7867 | 0.8209 | 0.7867 | 0.7920 | |
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| 0.2339 | 8.99 | 180 | 0.5053 | 0.8362 | 0.8426 | 0.8362 | 0.8355 | |
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| 0.2284 | 9.99 | 200 | 0.5070 | 0.8230 | 0.8220 | 0.8230 | 0.8187 | |
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| 0.1824 | 10.99 | 220 | 0.5780 | 0.8006 | 0.8138 | 0.8006 | 0.8035 | |
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| 0.1561 | 11.99 | 240 | 0.5429 | 0.8253 | 0.8197 | 0.8253 | 0.8218 | |
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| 0.1229 | 12.99 | 260 | 0.5325 | 0.8331 | 0.8296 | 0.8331 | 0.8303 | |
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| 0.1232 | 13.99 | 280 | 0.5595 | 0.8277 | 0.8290 | 0.8277 | 0.8273 | |
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| 0.118 | 14.99 | 300 | 0.5974 | 0.8292 | 0.8345 | 0.8292 | 0.8299 | |
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| 0.11 | 15.99 | 320 | 0.5796 | 0.8253 | 0.8228 | 0.8253 | 0.8231 | |
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| 0.0948 | 16.99 | 340 | 0.5581 | 0.8346 | 0.8358 | 0.8346 | 0.8349 | |
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| 0.0985 | 17.99 | 360 | 0.5700 | 0.8338 | 0.8301 | 0.8338 | 0.8318 | |
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| 0.0821 | 18.99 | 380 | 0.5756 | 0.8331 | 0.8343 | 0.8331 | 0.8335 | |
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| 0.0813 | 19.99 | 400 | 0.5984 | 0.8323 | 0.8312 | 0.8323 | 0.8315 | |
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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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