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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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model-index: |
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- name: plant-seedlings-resnet-152 |
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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: 0.9146715776550031 |
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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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# plant-seedlings-resnet-152 |
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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: 0.2604 |
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- Accuracy: 0.9147 |
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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: 0.0002 |
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- train_batch_size: 16 |
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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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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 2.175 | 0.53 | 100 | 2.1135 | 0.3247 | |
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| 1.146 | 1.06 | 200 | 1.0761 | 0.6654 | |
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| 0.8299 | 1.6 | 300 | 0.7586 | 0.7391 | |
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| 0.7896 | 2.13 | 400 | 0.7093 | 0.7680 | |
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| 0.7327 | 2.66 | 500 | 0.5140 | 0.8207 | |
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| 0.5207 | 3.19 | 600 | 0.5375 | 0.8183 | |
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| 0.6465 | 3.72 | 700 | 0.4620 | 0.8465 | |
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| 0.2745 | 4.26 | 800 | 0.4784 | 0.8324 | |
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| 0.5366 | 4.79 | 900 | 0.4804 | 0.8355 | |
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| 0.4467 | 5.32 | 1000 | 0.4354 | 0.8551 | |
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| 0.3604 | 5.85 | 1100 | 0.3950 | 0.8680 | |
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| 0.2511 | 6.38 | 1200 | 0.4279 | 0.8594 | |
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| 0.326 | 6.91 | 1300 | 0.3677 | 0.8852 | |
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| 0.3444 | 7.45 | 1400 | 0.3539 | 0.8748 | |
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| 0.4015 | 7.98 | 1500 | 0.3161 | 0.8950 | |
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| 0.2821 | 8.51 | 1600 | 0.4394 | 0.8686 | |
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| 0.435 | 9.04 | 1700 | 0.3408 | 0.8920 | |
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| 0.3318 | 9.57 | 1800 | 0.3886 | 0.8778 | |
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| 0.2441 | 10.11 | 1900 | 0.2854 | 0.9042 | |
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| 0.2467 | 10.64 | 2000 | 0.3248 | 0.8883 | |
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| 0.2082 | 11.17 | 2100 | 0.3080 | 0.8956 | |
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| 0.1983 | 11.7 | 2200 | 0.3394 | 0.8963 | |
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| 0.2609 | 12.23 | 2300 | 0.3582 | 0.8870 | |
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| 0.2055 | 12.77 | 2400 | 0.3330 | 0.8963 | |
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| 0.3476 | 13.3 | 2500 | 0.2852 | 0.9091 | |
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| 0.223 | 13.83 | 2600 | 0.3115 | 0.8999 | |
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| 0.2307 | 14.36 | 2700 | 0.2986 | 0.9098 | |
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| 0.3113 | 14.89 | 2800 | 0.3103 | 0.8993 | |
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| 0.1792 | 15.43 | 2900 | 0.2862 | 0.9098 | |
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| 0.1685 | 15.96 | 3000 | 0.2935 | 0.9055 | |
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| 0.2429 | 16.49 | 3100 | 0.2882 | 0.9122 | |
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| 0.2548 | 17.02 | 3200 | 0.2748 | 0.9165 | |
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| 0.3561 | 17.55 | 3300 | 0.2684 | 0.9171 | |
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| 0.1982 | 18.09 | 3400 | 0.2647 | 0.9147 | |
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| 0.1638 | 18.62 | 3500 | 0.2509 | 0.9171 | |
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| 0.2404 | 19.15 | 3600 | 0.2936 | 0.9165 | |
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| 0.2424 | 19.68 | 3700 | 0.2604 | 0.9147 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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