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carolinetfls
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plant-seedlings-vit-freeze-0-6-aug-3-whole-data-train

Image Classification
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PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results
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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-freeze-0-6-aug-3-whole-data-train
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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: 1.0
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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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+ # plant-seedlings-freeze-0-6-aug-3-whole-data-train
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0001
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+ - Accuracy: 1.0
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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: 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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+
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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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+ | 0.6146 | 0.16 | 100 | 0.0402 | 1.0 |
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+ | 0.6062 | 0.31 | 200 | 0.2393 | 1.0 |
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+ | 0.4847 | 0.47 | 300 | 0.2164 | 1.0 |
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+ | 0.5282 | 0.63 | 400 | 0.0427 | 1.0 |
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+ | 0.4153 | 0.79 | 500 | 1.0996 | 0.0 |
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+ | 0.3295 | 0.94 | 600 | 0.0499 | 1.0 |
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+ | 0.3541 | 1.1 | 700 | 0.0009 | 1.0 |
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+ | 0.4617 | 1.26 | 800 | 0.0106 | 1.0 |
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+ | 0.3014 | 1.42 | 900 | 0.0045 | 1.0 |
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+ | 0.3558 | 1.57 | 1000 | 0.0038 | 1.0 |
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+ | 0.2357 | 1.73 | 1100 | 0.0140 | 1.0 |
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+ | 0.3055 | 1.89 | 1200 | 0.0809 | 1.0 |
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+ | 0.3278 | 2.04 | 1300 | 0.0077 | 1.0 |
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+ | 0.3075 | 2.2 | 1400 | 0.0059 | 1.0 |
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+ | 0.3462 | 2.36 | 1500 | 0.0377 | 1.0 |
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+ | 0.2968 | 2.52 | 1600 | 0.0082 | 1.0 |
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+ | 0.3392 | 2.67 | 1700 | 0.8628 | 0.0 |
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+ | 0.2155 | 2.83 | 1800 | 0.0022 | 1.0 |
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+ | 0.3521 | 2.99 | 1900 | 0.0671 | 1.0 |
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+ | 0.3968 | 3.14 | 2000 | 0.0014 | 1.0 |
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+ | 0.32 | 3.3 | 2100 | 0.0075 | 1.0 |
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+ | 0.1787 | 3.46 | 2200 | 0.0015 | 1.0 |
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+ | 0.2598 | 3.62 | 2300 | 0.0086 | 1.0 |
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+ | 0.3424 | 3.77 | 2400 | 0.0008 | 1.0 |
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+ | 0.2371 | 3.93 | 2500 | 0.0054 | 1.0 |
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+ | 0.2773 | 4.09 | 2600 | 0.0028 | 1.0 |
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+ | 0.3192 | 4.25 | 2700 | 0.0088 | 1.0 |
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+ | 0.2173 | 4.4 | 2800 | 0.1174 | 1.0 |
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+ | 0.2181 | 4.56 | 2900 | 0.0056 | 1.0 |
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+ | 0.2476 | 4.72 | 3000 | 0.0006 | 1.0 |
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+ | 0.2417 | 4.87 | 3100 | 0.0005 | 1.0 |
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+ | 0.1915 | 5.03 | 3200 | 0.0002 | 1.0 |
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+ | 0.149 | 5.19 | 3300 | 0.0004 | 1.0 |
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+ | 0.1618 | 5.35 | 3400 | 0.1542 | 1.0 |
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+ | 0.1752 | 5.5 | 3500 | 0.0001 | 1.0 |
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+ | 0.1094 | 5.66 | 3600 | 0.0045 | 1.0 |
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+ | 0.2532 | 5.82 | 3700 | 0.0016 | 1.0 |
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+ | 0.1606 | 5.97 | 3800 | 0.0004 | 1.0 |
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+ | 0.1781 | 6.13 | 3900 | 0.0007 | 1.0 |
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+ | 0.1459 | 6.29 | 4000 | 0.0003 | 1.0 |
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+ | 0.2357 | 6.45 | 4100 | 2.6113 | 0.0 |
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+ | 0.2524 | 6.6 | 4200 | 0.0003 | 1.0 |
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+ | 0.1708 | 6.76 | 4300 | 0.0006 | 1.0 |
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+ | 0.1875 | 6.92 | 4400 | 0.0011 | 1.0 |
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+ | 0.1462 | 7.08 | 4500 | 0.0004 | 1.0 |
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+ | 0.1534 | 7.23 | 4600 | 0.0002 | 1.0 |
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+ | 0.2834 | 7.39 | 4700 | 0.0003 | 1.0 |
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+ | 0.2264 | 7.55 | 4800 | 0.0001 | 1.0 |
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+ | 0.1007 | 7.7 | 4900 | 0.0001 | 1.0 |
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+ | 0.2376 | 7.86 | 5000 | 0.0006 | 1.0 |
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+ | 0.2233 | 8.02 | 5100 | 0.0002 | 1.0 |
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+ | 0.1804 | 8.18 | 5200 | 0.0034 | 1.0 |
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+ | 0.185 | 8.33 | 5300 | 0.0002 | 1.0 |
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+ | 0.1149 | 8.49 | 5400 | 0.0007 | 1.0 |
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+ | 0.2048 | 8.65 | 5500 | 0.0009 | 1.0 |
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+ | 0.0786 | 8.81 | 5600 | 0.9478 | 0.0 |
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+ | 0.2222 | 8.96 | 5700 | 0.0007 | 1.0 |
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+ | 0.1289 | 9.12 | 5800 | 0.0009 | 1.0 |
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+ | 0.2248 | 9.28 | 5900 | 0.0005 | 1.0 |
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+ | 0.0987 | 9.43 | 6000 | 0.0002 | 1.0 |
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+ | 0.2897 | 9.59 | 6100 | 0.0002 | 1.0 |
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+ | 0.2023 | 9.75 | 6200 | 0.0042 | 1.0 |
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+ | 0.1481 | 9.91 | 6300 | 0.0003 | 1.0 |
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+ | 0.1224 | 10.06 | 6400 | 0.0009 | 1.0 |
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+ | 0.1353 | 10.22 | 6500 | 0.0080 | 1.0 |
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+ | 0.0659 | 10.38 | 6600 | 0.0006 | 1.0 |
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+ | 0.1692 | 10.53 | 6700 | 0.0005 | 1.0 |
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+ | 0.1713 | 10.69 | 6800 | 0.0006 | 1.0 |
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+ | 0.1131 | 10.85 | 6900 | 0.0012 | 1.0 |
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+ | 0.2325 | 11.01 | 7000 | 0.0003 | 1.0 |
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+ | 0.0817 | 11.16 | 7100 | 0.0003 | 1.0 |
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+ | 0.1854 | 11.32 | 7200 | 0.0001 | 1.0 |
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+ | 0.0956 | 11.48 | 7300 | 0.0002 | 1.0 |
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+ | 0.0758 | 11.64 | 7400 | 0.0127 | 1.0 |
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+ | 0.0928 | 11.79 | 7500 | 0.0002 | 1.0 |
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+ | 0.1563 | 11.95 | 7600 | 0.0004 | 1.0 |
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+ | 0.0596 | 12.11 | 7700 | 0.0003 | 1.0 |
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+ | 0.1266 | 12.26 | 7800 | 0.0031 | 1.0 |
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+ | 0.1788 | 12.42 | 7900 | 0.0002 | 1.0 |
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+ | 0.1663 | 12.58 | 8000 | 0.0071 | 1.0 |
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+ | 0.064 | 12.74 | 8100 | 0.0003 | 1.0 |
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+ | 0.1459 | 12.89 | 8200 | 0.0005 | 1.0 |
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+ | 0.1237 | 13.05 | 8300 | 0.0001 | 1.0 |
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+ | 0.1334 | 13.21 | 8400 | 0.0001 | 1.0 |
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+ | 0.0802 | 13.36 | 8500 | 0.0001 | 1.0 |
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+ | 0.1418 | 13.52 | 8600 | 0.0000 | 1.0 |
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+ | 0.048 | 13.68 | 8700 | 0.0001 | 1.0 |
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+ | 0.1267 | 13.84 | 8800 | 0.0121 | 1.0 |
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+ | 0.1298 | 13.99 | 8900 | 0.0001 | 1.0 |
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+ | 0.16 | 14.15 | 9000 | 0.0001 | 1.0 |
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+ | 0.1295 | 14.31 | 9100 | 0.0001 | 1.0 |
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+ | 0.1714 | 14.47 | 9200 | 0.0001 | 1.0 |
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+ | 0.1377 | 14.62 | 9300 | 0.0001 | 1.0 |
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+ | 0.1336 | 14.78 | 9400 | 0.0001 | 1.0 |
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+ | 0.1293 | 14.94 | 9500 | 0.0001 | 1.0 |
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+ | 0.111 | 15.09 | 9600 | 0.0001 | 1.0 |
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+ | 0.0818 | 15.25 | 9700 | 0.0000 | 1.0 |
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+ | 0.1884 | 15.41 | 9800 | 0.0001 | 1.0 |
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+ | 0.1004 | 15.57 | 9900 | 0.0002 | 1.0 |
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+ | 0.1029 | 15.72 | 10000 | 0.0000 | 1.0 |
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+ | 0.0772 | 15.88 | 10100 | 0.0000 | 1.0 |
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+ | 0.1573 | 16.04 | 10200 | 0.0001 | 1.0 |
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+ | 0.0748 | 16.19 | 10300 | 0.0001 | 1.0 |
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+ | 0.088 | 16.35 | 10400 | 0.0001 | 1.0 |
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+ | 0.1062 | 16.51 | 10500 | 0.0001 | 1.0 |
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+ | 0.0237 | 16.67 | 10600 | 0.0001 | 1.0 |
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+ | 0.0729 | 16.82 | 10700 | 0.0000 | 1.0 |
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+ | 0.1028 | 16.98 | 10800 | 0.0001 | 1.0 |
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+ | 0.0423 | 17.14 | 10900 | 0.0000 | 1.0 |
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+ | 0.0922 | 17.3 | 11000 | 0.0002 | 1.0 |
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+ | 0.0788 | 17.45 | 11100 | 0.0001 | 1.0 |
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+ | 0.0357 | 17.61 | 11200 | 0.0001 | 1.0 |
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+ | 0.0519 | 17.77 | 11300 | 0.0000 | 1.0 |
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+ | 0.108 | 17.92 | 11400 | 0.0001 | 1.0 |
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+ | 0.1746 | 18.08 | 11500 | 0.1221 | 1.0 |
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+ | 0.1 | 18.24 | 11600 | 0.0006 | 1.0 |
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+ | 0.0798 | 18.4 | 11700 | 0.0001 | 1.0 |
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+ | 0.0118 | 18.55 | 11800 | 0.0001 | 1.0 |
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+ | 0.1151 | 18.71 | 11900 | 0.0001 | 1.0 |
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+ | 0.0617 | 18.87 | 12000 | 0.0001 | 1.0 |
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+ | 0.1577 | 19.03 | 12100 | 0.0001 | 1.0 |
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+ | 0.1928 | 19.18 | 12200 | 0.0001 | 1.0 |
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+ | 0.0462 | 19.34 | 12300 | 0.0001 | 1.0 |
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+ | 0.0461 | 19.5 | 12400 | 0.3145 | 1.0 |
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+ | 0.0454 | 19.65 | 12500 | 0.0001 | 1.0 |
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+ | 0.0637 | 19.81 | 12600 | 0.0001 | 1.0 |
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+ | 0.0733 | 19.97 | 12700 | 0.0001 | 1.0 |
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+
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+
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+ ### Framework versions
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+
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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