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--- |
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license: apache-2.0 |
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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: cvt-13-384-22k-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: 0.8315301391035549 |
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- name: Precision |
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type: precision |
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value: 0.8302128280229624 |
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- name: Recall |
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type: recall |
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value: 0.8315301391035549 |
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- name: F1 |
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type: f1 |
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value: 0.8292026505769348 |
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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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# cvt-13-384-22k-fv-finetuned-memes |
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This model is a fine-tuned version of [microsoft/cvt-13-384-22k](https://huggingface.co/microsoft/cvt-13-384-22k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5761 |
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- Accuracy: 0.8315 |
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- Precision: 0.8302 |
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- Recall: 0.8315 |
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- F1: 0.8292 |
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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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| 1.3821 | 0.99 | 20 | 1.2780 | 0.4969 | 0.5083 | 0.4969 | 0.4458 | |
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| 1.0785 | 1.99 | 40 | 0.8633 | 0.6669 | 0.6658 | 0.6669 | 0.6500 | |
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| 0.8862 | 2.99 | 60 | 0.7110 | 0.7218 | 0.7258 | 0.7218 | 0.7013 | |
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| 0.665 | 3.99 | 80 | 0.5515 | 0.8045 | 0.8137 | 0.8045 | 0.8050 | |
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| 0.6056 | 4.99 | 100 | 0.5956 | 0.7960 | 0.8041 | 0.7960 | 0.7846 | |
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| 0.4779 | 5.99 | 120 | 0.6229 | 0.7937 | 0.7945 | 0.7937 | 0.7857 | |
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| 0.4554 | 6.99 | 140 | 0.5355 | 0.8099 | 0.8126 | 0.8099 | 0.8086 | |
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| 0.4249 | 7.99 | 160 | 0.5447 | 0.8269 | 0.8275 | 0.8269 | 0.8236 | |
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| 0.4313 | 8.99 | 180 | 0.5530 | 0.8153 | 0.8140 | 0.8153 | 0.8132 | |
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| 0.423 | 9.99 | 200 | 0.5346 | 0.8238 | 0.8230 | 0.8238 | 0.8223 | |
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| 0.3997 | 10.99 | 220 | 0.5413 | 0.8338 | 0.8347 | 0.8338 | 0.8338 | |
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| 0.4095 | 11.99 | 240 | 0.5999 | 0.8207 | 0.8231 | 0.8207 | 0.8177 | |
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| 0.3979 | 12.99 | 260 | 0.5632 | 0.8284 | 0.8255 | 0.8284 | 0.8250 | |
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| 0.3408 | 13.99 | 280 | 0.5725 | 0.8207 | 0.8198 | 0.8207 | 0.8196 | |
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| 0.3828 | 14.99 | 300 | 0.5631 | 0.8277 | 0.8258 | 0.8277 | 0.8260 | |
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| 0.3595 | 15.99 | 320 | 0.6005 | 0.8308 | 0.8297 | 0.8308 | 0.8275 | |
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| 0.3789 | 16.99 | 340 | 0.5840 | 0.8300 | 0.8271 | 0.8300 | 0.8273 | |
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| 0.3545 | 17.99 | 360 | 0.5983 | 0.8246 | 0.8226 | 0.8246 | 0.8222 | |
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| 0.3472 | 18.99 | 380 | 0.5795 | 0.8416 | 0.8382 | 0.8416 | 0.8390 | |
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| 0.355 | 19.99 | 400 | 0.5761 | 0.8315 | 0.8302 | 0.8315 | 0.8292 | |
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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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