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swiftformer-xs-RH

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Files changed (11) hide show
  1. README.md +116 -0
  2. all_results.json +12 -0
  3. config.json +46 -0
  4. eval_results.json +8 -0
  5. model.safetensors +3 -0
  6. preprocessor_config.json +22 -0
  7. runs/Dec01_18-54-23_DESKTOP-SKBE9FB/events.out.tfevents.1733100864.DESKTOP-SKBE9FB.7036.0 +3 -0
  8. runs/Dec01_18-54-23_DESKTOP-SKBE9FB/events.out.tfevents.1733101309.DESKTOP-SKBE9FB.7036.1 +3 -0
  9. train_results.json +7 -0
  10. trainer_state.json +582 -0
  11. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ base_model: MBZUAI/swiftformer-xs
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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: swiftformer-xs-RH
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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: validation
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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: 0.8598130841121495
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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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+ # swiftformer-xs-RH
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+
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+ This model is a fine-tuned version of [MBZUAI/swiftformer-xs](https://huggingface.co/MBZUAI/swiftformer-xs) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4150
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+ - Accuracy: 0.8598
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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: 0.0003
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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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: 40
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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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+ | No log | 1.0 | 8 | 0.6926 | 0.6262 |
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+ | 0.693 | 2.0 | 16 | 0.6873 | 0.6822 |
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+ | 0.6899 | 3.0 | 24 | 0.6694 | 0.6729 |
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+ | 0.6732 | 4.0 | 32 | 0.6146 | 0.7290 |
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+ | 0.6256 | 5.0 | 40 | 0.7084 | 0.4953 |
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+ | 0.6256 | 6.0 | 48 | 0.6591 | 0.6355 |
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+ | 0.5796 | 7.0 | 56 | 0.5670 | 0.7383 |
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+ | 0.5253 | 8.0 | 64 | 0.5351 | 0.7196 |
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+ | 0.4713 | 9.0 | 72 | 0.4614 | 0.8411 |
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+ | 0.441 | 10.0 | 80 | 0.5826 | 0.7570 |
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+ | 0.441 | 11.0 | 88 | 0.4679 | 0.7850 |
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+ | 0.3803 | 12.0 | 96 | 0.4517 | 0.8411 |
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+ | 0.3513 | 13.0 | 104 | 0.4571 | 0.7757 |
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+ | 0.3043 | 14.0 | 112 | 0.4755 | 0.8037 |
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+ | 0.3172 | 15.0 | 120 | 0.5953 | 0.7944 |
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+ | 0.3172 | 16.0 | 128 | 0.5904 | 0.7383 |
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+ | 0.365 | 17.0 | 136 | 0.4213 | 0.8411 |
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+ | 0.279 | 18.0 | 144 | 0.4572 | 0.8037 |
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+ | 0.3092 | 19.0 | 152 | 0.4181 | 0.8131 |
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+ | 0.2667 | 20.0 | 160 | 0.4117 | 0.8224 |
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+ | 0.2667 | 21.0 | 168 | 0.4349 | 0.8037 |
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+ | 0.261 | 22.0 | 176 | 0.4185 | 0.8037 |
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+ | 0.2638 | 23.0 | 184 | 0.3989 | 0.8131 |
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+ | 0.2269 | 24.0 | 192 | 0.3971 | 0.8318 |
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+ | 0.2431 | 25.0 | 200 | 0.4784 | 0.8037 |
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+ | 0.2431 | 26.0 | 208 | 0.3763 | 0.8318 |
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+ | 0.2111 | 27.0 | 216 | 0.4088 | 0.8411 |
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+ | 0.2087 | 28.0 | 224 | 0.4024 | 0.8318 |
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+ | 0.1645 | 29.0 | 232 | 0.4161 | 0.8318 |
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+ | 0.2137 | 30.0 | 240 | 0.4128 | 0.8131 |
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+ | 0.2137 | 31.0 | 248 | 0.4004 | 0.8411 |
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+ | 0.2207 | 32.0 | 256 | 0.4206 | 0.8224 |
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+ | 0.1544 | 33.0 | 264 | 0.3622 | 0.8505 |
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+ | 0.197 | 34.0 | 272 | 0.4356 | 0.8411 |
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+ | 0.2168 | 35.0 | 280 | 0.4067 | 0.8318 |
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+ | 0.2168 | 36.0 | 288 | 0.3809 | 0.8224 |
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+ | 0.1631 | 37.0 | 296 | 0.3865 | 0.8411 |
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+ | 0.1913 | 38.0 | 304 | 0.4008 | 0.8318 |
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+ | 0.1595 | 39.0 | 312 | 0.3752 | 0.8318 |
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+ | 0.1694 | 40.0 | 320 | 0.4150 | 0.8598 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.2
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+ - Pytorch 2.1.2+cu118
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
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