Model save
Browse files- README.md +13 -13
- config.json +0 -1
- model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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datasets:
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- imagefolder
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: resnet-50-finetuned-FBark
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results:
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split: train
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args: default
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metrics:
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- name: Precision
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type: precision
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value: 0.990909090909091
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- name: Recall
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type: recall
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value: 0.9939393939393939
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- name: F1
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type: f1
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value: 0.9922719141323793
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- name: Accuracy
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type: accuracy
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value: 0.9906542056074766
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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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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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-
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- Precision: 0.9909
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- Recall: 0.9939
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- F1: 0.9923
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- Accuracy: 0.9907
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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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:
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### Training results
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datasets:
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- imagefolder
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metrics:
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- accuracy
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- f1
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- precision
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- recall
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model-index:
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- name: resnet-50-finetuned-FBark
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results:
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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: 0.9906542056074766
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- name: F1
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type: f1
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value: 0.9922719141323793
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- name: Precision
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type: precision
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value: 0.990909090909091
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- name: Recall
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type: recall
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value: 0.9939393939393939
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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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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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- Accuracy: 0.9907
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- F1: 0.9923
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- Loss: 0.0579
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- Precision: 0.9909
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- Recall: 0.9939
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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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: 35
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### Training results
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config.json
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"out_indices": [
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4
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"problem_type": "single_label_classification",
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"stage_names": [
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"stem",
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"stage1",
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"out_indices": [
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],
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"stage_names": [
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"stem",
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"stage1",
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model.safetensors
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size 94327540
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size 94327540
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training_args.bin
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