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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-152-fv-finetuned-memess |
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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: 0.767387944358578 |
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- name: Precision |
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type: precision |
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value: 0.7651125602674349 |
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- name: Recall |
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type: recall |
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value: 0.767387944358578 |
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- name: F1 |
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type: f1 |
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value: 0.7646848616766787 |
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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-152-fv-finetuned-memess |
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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: 0.6281 |
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- Accuracy: 0.7674 |
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- Precision: 0.7651 |
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- Recall: 0.7674 |
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- F1: 0.7647 |
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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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| 1.5902 | 0.99 | 20 | 1.5519 | 0.4938 | 0.3491 | 0.4938 | 0.3529 | |
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| 1.4694 | 1.99 | 40 | 1.3730 | 0.4892 | 0.4095 | 0.4892 | 0.3222 | |
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| 1.3129 | 2.99 | 60 | 1.2052 | 0.5301 | 0.3504 | 0.5301 | 0.4005 | |
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| 1.1831 | 3.99 | 80 | 1.1142 | 0.5587 | 0.4077 | 0.5587 | 0.4444 | |
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| 1.0581 | 4.99 | 100 | 0.9930 | 0.6012 | 0.5680 | 0.6012 | 0.5108 | |
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| 0.9464 | 5.99 | 120 | 0.9263 | 0.6507 | 0.6200 | 0.6507 | 0.6029 | |
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| 0.8581 | 6.99 | 140 | 0.8400 | 0.6917 | 0.6645 | 0.6917 | 0.6638 | |
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| 0.7739 | 7.99 | 160 | 0.7829 | 0.7087 | 0.6918 | 0.7087 | 0.6845 | |
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| 0.6762 | 8.99 | 180 | 0.7512 | 0.7318 | 0.7206 | 0.7318 | 0.7189 | |
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| 0.6162 | 9.99 | 200 | 0.7409 | 0.7264 | 0.7244 | 0.7264 | 0.7241 | |
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| 0.5546 | 10.99 | 220 | 0.6936 | 0.7465 | 0.7429 | 0.7465 | 0.7395 | |
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| 0.4633 | 11.99 | 240 | 0.6779 | 0.7473 | 0.7393 | 0.7473 | 0.7412 | |
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| 0.4373 | 12.99 | 260 | 0.6736 | 0.7573 | 0.7492 | 0.7573 | 0.7523 | |
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| 0.4074 | 13.99 | 280 | 0.6534 | 0.7566 | 0.7516 | 0.7566 | 0.7528 | |
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| 0.39 | 14.99 | 300 | 0.6521 | 0.7651 | 0.7603 | 0.7651 | 0.7608 | |
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| 0.3766 | 15.99 | 320 | 0.6499 | 0.7682 | 0.7607 | 0.7682 | 0.7630 | |
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| 0.3507 | 16.99 | 340 | 0.6497 | 0.7697 | 0.7686 | 0.7697 | 0.7686 | |
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| 0.3589 | 17.99 | 360 | 0.6519 | 0.7535 | 0.7485 | 0.7535 | 0.7502 | |
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| 0.3261 | 18.99 | 380 | 0.6449 | 0.7589 | 0.7597 | 0.7589 | 0.7585 | |
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| 0.3234 | 19.99 | 400 | 0.6281 | 0.7674 | 0.7651 | 0.7674 | 0.7647 | |
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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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