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test1

Image Classification
Transformers
Safetensors
mobilevit
Generated from Trainer
Inference Endpoints
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README.md ADDED
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+ ---
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+ license: other
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+ base_model: apple/mobilevit-small
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - f1
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+ - recall
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+ model-index:
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+ - name: test1
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+ results: []
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+ ---
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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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+
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+ # test1
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+
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+ This model is a fine-tuned version of [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.1366
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+ - Accuracy: 0.7952
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+ - F1: 0.7855
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+ - Recall: 0.7952
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0008
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall |
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+ |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:------:|
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+ | No log | 0.4554 | 500 | 0.6376 | 0.7896 | 0.7429 | 0.7896 |
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+ | 0.657 | 0.9107 | 1000 | 0.5839 | 0.8109 | 0.7679 | 0.8109 |
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+ | 0.657 | 1.3661 | 1500 | 0.7632 | 0.7322 | 0.7195 | 0.7322 |
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+ | 0.5653 | 1.8215 | 2000 | 0.5927 | 0.8144 | 0.7689 | 0.8144 |
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+ | 0.5653 | 2.2769 | 2500 | 0.5855 | 0.8174 | 0.7765 | 0.8174 |
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+ | 0.5128 | 2.7322 | 3000 | 0.5567 | 0.8210 | 0.7931 | 0.8210 |
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+ | 0.5128 | 3.1876 | 3500 | 0.5578 | 0.8214 | 0.7894 | 0.8214 |
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+ | 0.4648 | 3.6430 | 4000 | 0.5699 | 0.8236 | 0.7928 | 0.8236 |
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+ | 0.4648 | 4.0984 | 4500 | 0.6039 | 0.8053 | 0.7850 | 0.8053 |
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+ | 0.411 | 4.5537 | 5000 | 0.5662 | 0.8203 | 0.7989 | 0.8203 |
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+ | 0.411 | 5.0091 | 5500 | 0.6043 | 0.8252 | 0.7962 | 0.8252 |
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+ | 0.3532 | 5.4645 | 6000 | 0.6559 | 0.8060 | 0.7915 | 0.8060 |
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+ | 0.3532 | 5.9199 | 6500 | 0.6310 | 0.8175 | 0.7919 | 0.8175 |
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+ | 0.2847 | 6.3752 | 7000 | 0.7075 | 0.8029 | 0.7890 | 0.8029 |
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+ | 0.2847 | 6.8306 | 7500 | 0.8056 | 0.7743 | 0.7745 | 0.7743 |
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+ | 0.2265 | 7.2860 | 8000 | 0.8991 | 0.7957 | 0.7875 | 0.7957 |
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+ | 0.2265 | 7.7413 | 8500 | 0.8929 | 0.7904 | 0.7866 | 0.7904 |
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+ | 0.1626 | 8.1967 | 9000 | 0.9503 | 0.8022 | 0.7883 | 0.8022 |
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+ | 0.1626 | 8.6521 | 9500 | 1.0467 | 0.7904 | 0.7838 | 0.7904 |
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+ | 0.1099 | 9.1075 | 10000 | 1.0435 | 0.8009 | 0.7877 | 0.8009 |
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+ | 0.1099 | 9.5628 | 10500 | 1.1366 | 0.7952 | 0.7855 | 0.7952 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 2.2.1
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
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