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Rashed-Mamdi
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Rashed-vit-model

Image Classification
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  1. README.md +100 -0
  2. model.safetensors +1 -1
  3. runs/Jul31_05-30-27_ip-10-192-12-184/events.out.tfevents.1722403836.ip-10-192-12-184.2220.0 +2 -2
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224-in21k
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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: Rashed-vit-model
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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: 0.9888888888888889
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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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+ # Rashed-vit-model
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1128
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+ - Accuracy: 0.9889
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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: 0.0002
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+ - train_batch_size: 4
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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: 50
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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 |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|
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+ | 0.2279 | 1.9048 | 200 | 0.4485 | 0.9111 |
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+ | 0.1335 | 3.8095 | 400 | 0.0680 | 0.9889 |
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+ | 0.0061 | 5.7143 | 600 | 0.0047 | 1.0 |
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+ | 0.0025 | 7.6190 | 800 | 0.0606 | 0.9778 |
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+ | 0.0624 | 9.5238 | 1000 | 0.2500 | 0.9556 |
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+ | 0.0013 | 11.4286 | 1200 | 0.0868 | 0.9889 |
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+ | 0.001 | 13.3333 | 1400 | 0.0908 | 0.9889 |
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+ | 0.0008 | 15.2381 | 1600 | 0.0935 | 0.9889 |
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+ | 0.0006 | 17.1429 | 1800 | 0.0960 | 0.9889 |
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+ | 0.0005 | 19.0476 | 2000 | 0.0979 | 0.9889 |
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+ | 0.0004 | 20.9524 | 2200 | 0.0996 | 0.9889 |
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+ | 0.0004 | 22.8571 | 2400 | 0.1013 | 0.9889 |
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+ | 0.0003 | 24.7619 | 2600 | 0.1027 | 0.9889 |
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+ | 0.0003 | 26.6667 | 2800 | 0.1040 | 0.9889 |
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+ | 0.0003 | 28.5714 | 3000 | 0.1054 | 0.9889 |
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+ | 0.0002 | 30.4762 | 3200 | 0.1065 | 0.9889 |
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+ | 0.0002 | 32.3810 | 3400 | 0.1076 | 0.9889 |
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+ | 0.0002 | 34.2857 | 3600 | 0.1085 | 0.9889 |
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+ | 0.0002 | 36.1905 | 3800 | 0.1094 | 0.9889 |
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+ | 0.0002 | 38.0952 | 4000 | 0.1102 | 0.9889 |
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+ | 0.0002 | 40.0 | 4200 | 0.1109 | 0.9889 |
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+ | 0.0001 | 41.9048 | 4400 | 0.1115 | 0.9889 |
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+ | 0.0001 | 43.8095 | 4600 | 0.1120 | 0.9889 |
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+ | 0.0001 | 45.7143 | 4800 | 0.1124 | 0.9889 |
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+ | 0.0001 | 47.6190 | 5000 | 0.1126 | 0.9889 |
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+ | 0.0001 | 49.5238 | 5200 | 0.1128 | 0.9889 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.43.3
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
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