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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: resnet-50-FV2-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: 0.6452859350850078 |
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- name: Precision |
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type: precision |
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value: 0.5727919568038408 |
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- name: Recall |
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type: recall |
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value: 0.6452859350850078 |
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- name: F1 |
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type: f1 |
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value: 0.5963647629954705 |
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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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# resnet-50-FV2-finetuned-memes |
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This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9263 |
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- Accuracy: 0.6453 |
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- Precision: 0.5728 |
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- Recall: 0.6453 |
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- F1: 0.5964 |
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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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| 1.5763 | 0.99 | 20 | 1.5575 | 0.4281 | 0.2966 | 0.4281 | 0.2669 | |
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| 1.4761 | 1.99 | 40 | 1.4424 | 0.4343 | 0.1886 | 0.4343 | 0.2630 | |
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| 1.3563 | 2.99 | 60 | 1.3240 | 0.4343 | 0.1886 | 0.4343 | 0.2630 | |
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| 1.2824 | 3.99 | 80 | 1.2636 | 0.4389 | 0.3097 | 0.4389 | 0.2734 | |
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| 1.2315 | 4.99 | 100 | 1.2119 | 0.4529 | 0.3236 | 0.4529 | 0.3042 | |
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| 1.1956 | 5.99 | 120 | 1.1764 | 0.4900 | 0.3731 | 0.4900 | 0.3692 | |
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| 1.1452 | 6.99 | 140 | 1.1424 | 0.5147 | 0.3963 | 0.5147 | 0.4090 | |
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| 1.1076 | 7.99 | 160 | 1.1190 | 0.5371 | 0.4121 | 0.5371 | 0.4392 | |
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| 1.0679 | 8.99 | 180 | 1.0825 | 0.5719 | 0.4465 | 0.5719 | 0.4831 | |
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| 1.0432 | 9.99 | 200 | 1.0482 | 0.5750 | 0.5404 | 0.5750 | 0.4930 | |
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| 0.9903 | 10.99 | 220 | 1.0275 | 0.5958 | 0.5459 | 0.5958 | 0.5241 | |
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| 0.9675 | 11.99 | 240 | 1.0145 | 0.6051 | 0.5350 | 0.6051 | 0.5379 | |
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| 0.9335 | 12.99 | 260 | 0.9860 | 0.6175 | 0.5537 | 0.6175 | 0.5527 | |
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| 0.9157 | 13.99 | 280 | 0.9683 | 0.6105 | 0.5386 | 0.6105 | 0.5504 | |
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| 0.8901 | 14.99 | 300 | 0.9558 | 0.6352 | 0.5686 | 0.6352 | 0.5833 | |
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| 0.8722 | 15.99 | 320 | 0.9382 | 0.6345 | 0.5657 | 0.6345 | 0.5807 | |
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| 0.854 | 16.99 | 340 | 0.9322 | 0.6376 | 0.5623 | 0.6376 | 0.5856 | |
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| 0.8494 | 17.99 | 360 | 0.9287 | 0.6422 | 0.6675 | 0.6422 | 0.5918 | |
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| 0.8652 | 18.99 | 380 | 0.9212 | 0.6399 | 0.5640 | 0.6399 | 0.5863 | |
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| 0.846 | 19.99 | 400 | 0.9263 | 0.6453 | 0.5728 | 0.6453 | 0.5964 | |
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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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