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google-vit-base-patch16-224-in21k-batch_16_epoch_4_classes_24

Image Classification
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  1. README.md +132 -0
  2. config.json +76 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +36 -0
  5. runs/May14_05-18-52_fb914edf4077/events.out.tfevents.1715663941.fb914edf4077.34.0 +3 -0
  6. training_args.bin +3 -0
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: google-vit-base-patch16-224-in21k-batch_16_epoch_4_classes_24
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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: 0.9683908045977011
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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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+ # google-vit-base-patch16-224-in21k-batch_16_epoch_4_classes_24
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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: 0.1385
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+ - Accuracy: 0.9684
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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: 4
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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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+ | 0.7768 | 0.07 | 100 | 0.7113 | 0.9282 |
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+ | 0.3925 | 0.14 | 200 | 0.4597 | 0.8908 |
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+ | 0.2437 | 0.21 | 300 | 0.3130 | 0.9282 |
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+ | 0.2105 | 0.28 | 400 | 0.3497 | 0.9023 |
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+ | 0.1744 | 0.35 | 500 | 0.3150 | 0.9124 |
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+ | 0.167 | 0.42 | 600 | 0.2949 | 0.9239 |
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+ | 0.1176 | 0.49 | 700 | 0.3204 | 0.9195 |
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+ | 0.077 | 0.56 | 800 | 0.3104 | 0.9253 |
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+ | 0.1113 | 0.63 | 900 | 0.1950 | 0.9511 |
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+ | 0.1172 | 0.7 | 1000 | 0.2692 | 0.9239 |
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+ | 0.0971 | 0.77 | 1100 | 0.3097 | 0.9267 |
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+ | 0.1143 | 0.84 | 1200 | 0.2072 | 0.9454 |
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+ | 0.1545 | 0.91 | 1300 | 0.2859 | 0.9253 |
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+ | 0.0794 | 0.97 | 1400 | 0.2893 | 0.9224 |
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+ | 0.0951 | 1.04 | 1500 | 0.2094 | 0.9483 |
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+ | 0.0657 | 1.11 | 1600 | 0.2714 | 0.9353 |
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+ | 0.0068 | 1.18 | 1700 | 0.2305 | 0.9425 |
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+ | 0.0511 | 1.25 | 1800 | 0.1682 | 0.9555 |
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+ | 0.0629 | 1.32 | 1900 | 0.2328 | 0.9454 |
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+ | 0.0373 | 1.39 | 2000 | 0.3263 | 0.9310 |
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+ | 0.0885 | 1.46 | 2100 | 0.2341 | 0.9454 |
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+ | 0.0433 | 1.53 | 2200 | 0.2670 | 0.9397 |
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+ | 0.0046 | 1.6 | 2300 | 0.2308 | 0.9468 |
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+ | 0.0054 | 1.67 | 2400 | 0.3182 | 0.9296 |
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+ | 0.0952 | 1.74 | 2500 | 0.2297 | 0.9411 |
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+ | 0.1361 | 1.81 | 2600 | 0.2058 | 0.9454 |
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+ | 0.1124 | 1.88 | 2700 | 0.1656 | 0.9598 |
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+ | 0.0339 | 1.95 | 2800 | 0.1933 | 0.9526 |
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+ | 0.0021 | 2.02 | 2900 | 0.1475 | 0.9569 |
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+ | 0.0248 | 2.09 | 3000 | 0.1806 | 0.9583 |
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+ | 0.0013 | 2.16 | 3100 | 0.1899 | 0.9526 |
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+ | 0.0035 | 2.23 | 3200 | 0.1391 | 0.9641 |
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+ | 0.0358 | 2.3 | 3300 | 0.1593 | 0.9684 |
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+ | 0.0026 | 2.37 | 3400 | 0.1927 | 0.9612 |
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+ | 0.001 | 2.44 | 3500 | 0.1756 | 0.9583 |
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+ | 0.0113 | 2.51 | 3600 | 0.1512 | 0.9713 |
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+ | 0.0009 | 2.58 | 3700 | 0.1540 | 0.9698 |
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+ | 0.0498 | 2.65 | 3800 | 0.1498 | 0.9641 |
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+ | 0.0084 | 2.72 | 3900 | 0.1435 | 0.9655 |
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+ | 0.001 | 2.79 | 4000 | 0.1199 | 0.9713 |
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+ | 0.0011 | 2.86 | 4100 | 0.1301 | 0.9655 |
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+ | 0.003 | 2.92 | 4200 | 0.1350 | 0.9727 |
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+ | 0.0025 | 2.99 | 4300 | 0.1764 | 0.9583 |
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+ | 0.0006 | 3.06 | 4400 | 0.1564 | 0.9713 |
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+ | 0.0006 | 3.13 | 4500 | 0.1524 | 0.9713 |
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+ | 0.0006 | 3.2 | 4600 | 0.1515 | 0.9727 |
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+ | 0.0006 | 3.27 | 4700 | 0.1633 | 0.9741 |
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+ | 0.0005 | 3.34 | 4800 | 0.1404 | 0.9713 |
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+ | 0.0005 | 3.41 | 4900 | 0.1586 | 0.9684 |
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+ | 0.0005 | 3.48 | 5000 | 0.1576 | 0.9655 |
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+ | 0.0005 | 3.55 | 5100 | 0.1505 | 0.9684 |
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+ | 0.0153 | 3.62 | 5200 | 0.1369 | 0.9684 |
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+ | 0.0005 | 3.69 | 5300 | 0.1579 | 0.9670 |
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+ | 0.0005 | 3.76 | 5400 | 0.1451 | 0.9698 |
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+ | 0.0005 | 3.83 | 5500 | 0.1417 | 0.9698 |
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+ | 0.0005 | 3.9 | 5600 | 0.1380 | 0.9698 |
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+ | 0.0004 | 3.97 | 5700 | 0.1385 | 0.9684 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.3
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+ - Pytorch 2.1.2
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
config.json ADDED
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+ "12": "Mustard Hilsa(\u09b8\u09b0\u09b7\u09c7 \u0987\u09b2\u09bf\u09b6)",
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+ "13": "Nakshi Pitha(\u09a8\u0995\u09b6\u09bf \u09aa\u09bf\u09a0\u09be)",
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+ "14": "Panta Ilish(\u09aa\u09be\u09a8\u09cd\u09a4\u09be \u0987\u09b2\u09bf\u09b6)",
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+ "15": "Patishapta Pitha(\u09aa\u09be\u099f\u09bf\u09b8\u09be\u09aa\u099f\u09be)",
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+ "19": "Roshmalai(\u09b0\u09b8\u09ae\u09be\u09b2\u09be\u0987)",
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+ "2": "Chicken Pulao(\u09ae\u09cb\u09b0\u0997 \u09aa\u09cb\u09b2\u09be\u0993)",
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+ "20": "Shahi Tukra(\u09b6\u09be\u09b9\u09bf \u099f\u09c1\u0995\u09b0\u09be)",
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+ "21": "Shingara(\u09b8\u09bf\u0999\u09cd\u0997\u09be\u09b0\u09be)",
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+ "22": "Sweet Yogurt(\u09ae\u09bf\u09b7\u09cd\u099f\u09bf \u09a6\u0987)",
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+ "3": "Chickpease Bhuna(\u099b\u09cb\u09b2\u09be\u09ad\u09c1\u09a8\u09be)",
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+ "4": "Egg Curry(\u09a1\u09bf\u09ae\u09ad\u09c1\u09a8\u09be)",
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+ "7": "Haleem(\u09b9\u09be\u09b2\u09bf\u09ae)",
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+ "8": "Jalebi(\u099c\u09bf\u09b2\u09be\u09aa\u09c0)",
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+ "9": "Kala Bhuna(\u0995\u09be\u09b2\u09be \u09ad\u09c1\u09a8\u09be)"
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+ },
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+ "image_size": 224,
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+ "initializer_range": 0.02,
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