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ViTL_16_384_1e_4_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/May23_18-52-53_004147a0828c/events.out.tfevents.1716490380.004147a0828c.34.1 +3 -0
  6. runs/May23_18-53-32_004147a0828c/events.out.tfevents.1716490413.004147a0828c.34.2 +3 -0
  7. runs/May23_18-58-00_fdad6227c375/events.out.tfevents.1716490681.fdad6227c375.34.0 +3 -0
  8. 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-large-patch16-384
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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: Aradam_ViTL-16-384-2e-4-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: 0.9698275862068966
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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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+ # Aradam_ViTL-16-384-2e-4-batch_16_epoch_4_classes_24
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+
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+ This model is a fine-tuned version of [google/vit-large-patch16-384](https://huggingface.co/google/vit-large-patch16-384) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1097
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+ - Accuracy: 0.9698
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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: 8
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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: 2
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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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+ | 1.1511 | 0.03 | 100 | 0.8900 | 0.7471 |
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+ | 0.8497 | 0.07 | 200 | 0.8558 | 0.7687 |
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+ | 0.6297 | 0.1 | 300 | 0.5995 | 0.8132 |
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+ | 0.5735 | 0.14 | 400 | 0.4456 | 0.8649 |
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+ | 0.307 | 0.17 | 500 | 0.4031 | 0.8851 |
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+ | 0.3961 | 0.21 | 600 | 0.4865 | 0.8506 |
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+ | 0.6511 | 0.24 | 700 | 0.5270 | 0.8491 |
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+ | 0.4526 | 0.28 | 800 | 0.6105 | 0.8376 |
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+ | 0.4071 | 0.31 | 900 | 0.3936 | 0.8937 |
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+ | 0.2729 | 0.35 | 1000 | 0.3287 | 0.8994 |
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+ | 0.4277 | 0.38 | 1100 | 0.5402 | 0.8621 |
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+ | 0.2588 | 0.42 | 1200 | 0.3344 | 0.9023 |
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+ | 0.3034 | 0.45 | 1300 | 0.3269 | 0.8922 |
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+ | 0.2463 | 0.49 | 1400 | 0.4931 | 0.8563 |
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+ | 0.1999 | 0.52 | 1500 | 0.3622 | 0.9037 |
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+ | 0.1483 | 0.56 | 1600 | 0.3114 | 0.9066 |
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+ | 0.1266 | 0.59 | 1700 | 0.3893 | 0.8894 |
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+ | 0.1131 | 0.63 | 1800 | 0.2696 | 0.9267 |
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+ | 0.4377 | 0.66 | 1900 | 0.2953 | 0.9224 |
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+ | 0.1578 | 0.7 | 2000 | 0.3059 | 0.9109 |
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+ | 0.1273 | 0.73 | 2100 | 0.2474 | 0.9267 |
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+ | 0.077 | 0.77 | 2200 | 0.2231 | 0.9382 |
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+ | 0.0855 | 0.8 | 2300 | 0.2795 | 0.9368 |
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+ | 0.0756 | 0.84 | 2400 | 0.2858 | 0.9210 |
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+ | 0.2635 | 0.87 | 2500 | 0.2563 | 0.9353 |
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+ | 0.1622 | 0.91 | 2600 | 0.2727 | 0.9325 |
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+ | 0.1941 | 0.94 | 2700 | 0.2450 | 0.9239 |
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+ | 0.0144 | 0.98 | 2800 | 0.2113 | 0.9454 |
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+ | 0.0617 | 1.01 | 2900 | 0.1612 | 0.9454 |
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+ | 0.0188 | 1.04 | 3000 | 0.2029 | 0.9425 |
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+ | 0.0731 | 1.08 | 3100 | 0.1762 | 0.9612 |
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+ | 0.0846 | 1.11 | 3200 | 0.1612 | 0.9569 |
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+ | 0.0586 | 1.15 | 3300 | 0.2737 | 0.9353 |
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+ | 0.0258 | 1.18 | 3400 | 0.1310 | 0.9670 |
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+ | 0.0665 | 1.22 | 3500 | 0.1515 | 0.9540 |
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+ | 0.0143 | 1.25 | 3600 | 0.2254 | 0.9440 |
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+ | 0.0842 | 1.29 | 3700 | 0.2393 | 0.9468 |
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+ | 0.0019 | 1.32 | 3800 | 0.1660 | 0.9526 |
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+ | 0.013 | 1.36 | 3900 | 0.1413 | 0.9684 |
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+ | 0.0177 | 1.39 | 4000 | 0.1455 | 0.9641 |
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+ | 0.0128 | 1.43 | 4100 | 0.1291 | 0.9641 |
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+ | 0.0222 | 1.46 | 4200 | 0.1567 | 0.9526 |
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+ | 0.0017 | 1.5 | 4300 | 0.1640 | 0.9569 |
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+ | 0.0009 | 1.53 | 4400 | 0.1861 | 0.9612 |
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+ | 0.0007 | 1.57 | 4500 | 0.1440 | 0.9713 |
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+ | 0.0026 | 1.6 | 4600 | 0.0940 | 0.9784 |
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+ | 0.0006 | 1.64 | 4700 | 0.1282 | 0.9655 |
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+ | 0.0023 | 1.67 | 4800 | 0.1341 | 0.9698 |
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+ | 0.0002 | 1.71 | 4900 | 0.1099 | 0.9727 |
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+ | 0.0013 | 1.74 | 5000 | 0.0872 | 0.9756 |
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+ | 0.0001 | 1.78 | 5100 | 0.0908 | 0.9784 |
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+ | 0.0006 | 1.81 | 5200 | 0.1034 | 0.9727 |
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+ | 0.0009 | 1.85 | 5300 | 0.0940 | 0.9727 |
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+ | 0.0 | 1.88 | 5400 | 0.1236 | 0.9655 |
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+ | 0.0003 | 1.92 | 5500 | 0.1180 | 0.9684 |
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+ | 0.0001 | 1.95 | 5600 | 0.1091 | 0.9698 |
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+ | 0.0001 | 1.99 | 5700 | 0.1097 | 0.9698 |
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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
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+ {
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+ "_name_or_path": "google/vit-large-patch16-384",
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+ "architectures": [
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+ "ViTForImageClassification"
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+ ],
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+ "hidden_act": "gelu",
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+ "id2label": {
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+ "0": "Bhapa Pitha(\u09ad\u09be\u09aa\u09be \u09aa\u09bf\u09a0\u09be)",
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+ "1": "Biriyani(\u09ac\u09bf\u09b0\u09bf\u09df\u09be\u09a8\u09bf)",
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+ "10": "Khichuri(\u0996\u09bf\u099a\u09c1\u09a1\u09bc\u09bf)",
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+ "11": "Malpua Pitha(\u09ae\u09be\u09b2\u09aa\u09c1\u09df\u09be \u09aa\u09bf\u09a0\u09be)",
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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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+ "16": "Prawn Malai Curry(\u099a\u09bf\u0982\u09dc\u09bf \u09ae\u09be\u09b2\u09be\u0987\u0995\u09be\u09b0\u09c0)",
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+ "17": "Rasgulla(\u09b0\u09b8\u0997\u09cb\u09b2\u09cd\u09b2\u09be)",
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+ "18": "Rose Cookies(\u09ab\u09c1\u09b2\u099d\u09c1\u09b0\u09bf \u09aa\u09bf\u09a0\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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+ "23": "Tehari(\u09a4\u09c7\u09b9\u09be\u09b0\u09bf)",
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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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+ "5": "Falooda(\u09ab\u09be\u09b2\u09c1\u09a6\u09be)",
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+ "6": "Fuchka(\u09ab\u09c1\u099a\u0995\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": 384,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 4096,
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+ "label2id": {
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+ "Bhapa Pitha(\u09ad\u09be\u09aa\u09be \u09aa\u09bf\u09a0\u09be)": "0",
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+ "Biriyani(\u09ac\u09bf\u09b0\u09bf\u09df\u09be\u09a8\u09bf)": "1",
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+ "Egg Curry(\u09a1\u09bf\u09ae\u09ad\u09c1\u09a8\u09be)": "4",
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+ "Patishapta Pitha(\u09aa\u09be\u099f\u09bf\u09b8\u09be\u09aa\u099f\u09be)": "15",
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+ "Prawn Malai Curry(\u099a\u09bf\u0982\u09dc\u09bf \u09ae\u09be\u09b2\u09be\u0987\u0995\u09be\u09b0\u09c0)": "16",
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+ "Roshmalai(\u09b0\u09b8\u09ae\u09be\u09b2\u09be\u0987)": "19",
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+ "Shingara(\u09b8\u09bf\u0999\u09cd\u0997\u09be\u09b0\u09be)": "21",
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+ "Sweet Yogurt(\u09ae\u09bf\u09b7\u09cd\u099f\u09bf \u09a6\u0987)": "22",
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+ "transformers_version": "4.39.3"
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