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update model card README.md
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README.md
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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: finetuned-ConvNext-Indian-food
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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: 0.9309245483528161
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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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# finetuned-ConvNext-Indian-food
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This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3046
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- Accuracy: 0.9309
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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: 10
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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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| 1.3145 | 0.3 | 100 | 1.0460 | 0.8151 |
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| 0.6694 | 0.6 | 200 | 0.5439 | 0.8757 |
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| 0.5057 | 0.9 | 300 | 0.4398 | 0.8831 |
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| 0.4381 | 1.2 | 400 | 0.4286 | 0.8820 |
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| 0.4376 | 1.5 | 500 | 0.3400 | 0.9044 |
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| 0.2499 | 1.8 | 600 | 0.3312 | 0.9065 |
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| 0.2802 | 2.1 | 700 | 0.3338 | 0.9033 |
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| 0.3014 | 2.4 | 800 | 0.3572 | 0.8948 |
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| 0.2508 | 2.7 | 900 | 0.3432 | 0.9022 |
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| 0.2012 | 3.0 | 1000 | 0.3060 | 0.9086 |
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| 0.2634 | 3.3 | 1100 | 0.3451 | 0.9086 |
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| 0.2483 | 3.6 | 1200 | 0.3550 | 0.9044 |
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| 0.2273 | 3.9 | 1300 | 0.2977 | 0.9107 |
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| 0.1214 | 4.2 | 1400 | 0.3265 | 0.9160 |
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| 0.2048 | 4.5 | 1500 | 0.3126 | 0.9214 |
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| 0.0997 | 4.8 | 1600 | 0.3164 | 0.9160 |
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| 0.1145 | 5.11 | 1700 | 0.3055 | 0.9139 |
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| 0.1578 | 5.41 | 1800 | 0.3195 | 0.9171 |
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| 0.0615 | 5.71 | 1900 | 0.3401 | 0.9107 |
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| 0.1537 | 6.01 | 2000 | 0.3428 | 0.9097 |
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| 0.1278 | 6.31 | 2100 | 0.3058 | 0.9192 |
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| 0.1274 | 6.61 | 2200 | 0.3189 | 0.9192 |
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| 0.0877 | 6.91 | 2300 | 0.3370 | 0.9182 |
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| 0.1058 | 7.21 | 2400 | 0.3225 | 0.9192 |
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| 0.1742 | 7.51 | 2500 | 0.3341 | 0.9214 |
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| 0.0949 | 7.81 | 2600 | 0.3126 | 0.9256 |
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| 0.1732 | 8.11 | 2700 | 0.3078 | 0.9235 |
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| 0.0894 | 8.41 | 2800 | 0.3098 | 0.9267 |
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| 0.1257 | 8.71 | 2900 | 0.3030 | 0.9320 |
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| 0.1747 | 9.01 | 3000 | 0.3106 | 0.9256 |
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| 0.2119 | 9.31 | 3100 | 0.3037 | 0.9299 |
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| 0.1074 | 9.61 | 3200 | 0.3049 | 0.9277 |
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| 0.1275 | 9.91 | 3300 | 0.3046 | 0.9309 |
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### Framework versions
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- Transformers 4.22.2
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- Pytorch 1.12.1+cu113
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- Datasets 2.5.1
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- Tokenizers 0.12.1
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