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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: resnet152-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: 0.7557959814528593 |
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- name: Precision |
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type: precision |
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value: 0.7556690736625777 |
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- name: Recall |
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type: recall |
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value: 0.7557959814528593 |
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- name: F1 |
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type: f1 |
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value: 0.7545674798253312 |
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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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# resnet152-FV-finetuned-memes |
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This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6772 |
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- Accuracy: 0.7558 |
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- Precision: 0.7557 |
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- Recall: 0.7558 |
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- F1: 0.7546 |
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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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| 1.5739 | 0.99 | 20 | 1.5427 | 0.4521 | 0.3131 | 0.4521 | 0.2880 | |
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| 1.4353 | 1.99 | 40 | 1.3786 | 0.4490 | 0.3850 | 0.4490 | 0.2791 | |
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| 1.3026 | 2.99 | 60 | 1.2734 | 0.4799 | 0.3073 | 0.4799 | 0.3393 | |
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| 1.1579 | 3.99 | 80 | 1.1378 | 0.5278 | 0.4300 | 0.5278 | 0.4143 | |
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| 1.0276 | 4.99 | 100 | 1.0231 | 0.5734 | 0.4497 | 0.5734 | 0.4865 | |
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| 0.8826 | 5.99 | 120 | 0.9228 | 0.6252 | 0.5983 | 0.6252 | 0.5637 | |
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| 0.766 | 6.99 | 140 | 0.8441 | 0.6662 | 0.6474 | 0.6662 | 0.6320 | |
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| 0.6732 | 7.99 | 160 | 0.8009 | 0.6901 | 0.6759 | 0.6901 | 0.6704 | |
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| 0.5653 | 8.99 | 180 | 0.7535 | 0.7218 | 0.7141 | 0.7218 | 0.7129 | |
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| 0.4957 | 9.99 | 200 | 0.7317 | 0.7257 | 0.7248 | 0.7257 | 0.7200 | |
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| 0.4534 | 10.99 | 220 | 0.6808 | 0.7434 | 0.7405 | 0.7434 | 0.7390 | |
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| 0.3792 | 11.99 | 240 | 0.6949 | 0.7450 | 0.7454 | 0.7450 | 0.7399 | |
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| 0.3489 | 12.99 | 260 | 0.6746 | 0.7496 | 0.7511 | 0.7496 | 0.7474 | |
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| 0.3113 | 13.99 | 280 | 0.6637 | 0.7573 | 0.7638 | 0.7573 | 0.7579 | |
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| 0.2947 | 14.99 | 300 | 0.6451 | 0.7589 | 0.7667 | 0.7589 | 0.7610 | |
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| 0.2776 | 15.99 | 320 | 0.6754 | 0.7543 | 0.7565 | 0.7543 | 0.7525 | |
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| 0.2611 | 16.99 | 340 | 0.6808 | 0.7550 | 0.7607 | 0.7550 | 0.7529 | |
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| 0.2428 | 17.99 | 360 | 0.7005 | 0.7457 | 0.7497 | 0.7457 | 0.7404 | |
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| 0.2346 | 18.99 | 380 | 0.6597 | 0.7573 | 0.7642 | 0.7573 | 0.7590 | |
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| 0.2367 | 19.99 | 400 | 0.6772 | 0.7558 | 0.7557 | 0.7558 | 0.7546 | |
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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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