{
  "model": {
    "name": "indian_food_prediction",
    "base_model": "google/vit-base-patch16-224-in21k",
    "fine_tuning_checkpoint": "aishrica/indian_food_prediction",
    "published_as": "aishrica/indian_food_prediction",
    "architecture": "Vision Transformer (ViT-Base, patch 16, 224x224)",
    "framework": "PyTorch + HuggingFace Transformers",
    "trainable_params_millions": 85.86,
    "weights_size_mb": 343,
    "input": "RGB image -> 224x224, normalised -> tensor [3, 224, 224]",
    "output": "80-class logits -> softmax probabilities (argmax = dish)"
  },
  "dataset": {
    "source": "Kaggle - Indian Food Images Dataset",
    "total_images": 4000,
    "classes": 80,
    "per_class": 50,
    "train_images": 2400,
    "test_images": 1600,
    "split": "60 / 40 stratified, shuffled",
    "balanced": true
  },
  "augmentations": [
    "Resize 224x224",
    "RandomRotation(90)",
    "RandomAdjustSharpness(2)",
    "RandomHorizontalFlip(0.5)",
    "Normalize (ViT mean/std)"
  ],
  "hyperparameters": {
    "epochs": 20,
    "learning_rate": "1e-6",
    "train_batch_size": 64,
    "eval_batch_size": 32,
    "weight_decay": 0.02,
    "warmup_steps": 50,
    "optimizer_steps": 760,
    "runtime_min": 31
  },
  "metrics": {
    "accuracy": 0.7519,
    "macro_f1": 0.7352,
    "macro_precision": 0.7813,
    "macro_recall": 0.7519,
    "baseline_accuracy": 0.7494,
    "test_loss": 3.1539
  },
  "pipeline": [
    {
      "step": "Dataset",
      "detail": "4,000 photos - 80 dishes"
    },
    {
      "step": "Preprocess",
      "detail": "oversample - cast Image/ClassLabel"
    },
    {
      "step": "Augment",
      "detail": "resize - rotate - flip - sharpen - normalise"
    },
    {
      "step": "ViT Encoder",
      "detail": "ViT-Base patch16 - 85.9M params"
    },
    {
      "step": "Classifier Head",
      "detail": "80-class linear layer"
    },
    {
      "step": "Prediction",
      "detail": "top-3 dishes + confidence"
    },
    {
      "step": "Calorie DB",
      "detail": "label -> calories & category"
    }
  ],
  "limitations": [
    "Confidence scores are poorly calibrated (softmax is very flat, top score ~0.04).",
    "No separate held-out test set - the 40% split is used for both validation and test.",
    "Marginal fine-tuning effect (74.9% -> 75.2%) due to a near-converged base and lr=1e-6.",
    "Visually similar dishes (curries, milk-based sweets) are frequently confused.",
    "Small dataset (50 images/class) limits real-world generalisation.",
    "Single-dish assumption - no food segmentation or multi-item plate detection yet."
  ],
  "per_class": [
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      "label": "adhirasam",
      "precision": 0.9412,
      "recall": 0.8,
      "f1": 0.8649,
      "support": 20
    },
    {
      "label": "aloo_gobi",
      "precision": 0.7857,
      "recall": 0.55,
      "f1": 0.6471,
      "support": 20
    },
    {
      "label": "aloo_matar",
      "precision": 0.85,
      "recall": 0.85,
      "f1": 0.85,
      "support": 20
    },
    {
      "label": "aloo_methi",
      "precision": 0.7407,
      "recall": 1.0,
      "f1": 0.8511,
      "support": 20
    },
    {
      "label": "aloo_shimla_mirch",
      "precision": 0.7619,
      "recall": 0.8,
      "f1": 0.7805,
      "support": 20
    },
    {
      "label": "aloo_tikki",
      "precision": 1.0,
      "recall": 0.75,
      "f1": 0.8571,
      "support": 20
    },
    {
      "label": "anarsa",
      "precision": 1.0,
      "recall": 0.7,
      "f1": 0.8235,
      "support": 20
    },
    {
      "label": "ariselu",
      "precision": 0.7692,
      "recall": 1.0,
      "f1": 0.8696,
      "support": 20
    },
    {
      "label": "bandar_laddu",
      "precision": 0.8333,
      "recall": 0.75,
      "f1": 0.7895,
      "support": 20
    },
    {
      "label": "basundi",
      "precision": 0.2254,
      "recall": 0.8,
      "f1": 0.3516,
      "support": 20
    },
    {
      "label": "bhatura",
      "precision": 0.76,
      "recall": 0.95,
      "f1": 0.8444,
      "support": 20
    },
    {
      "label": "bhindi_masala",
      "precision": 0.8636,
      "recall": 0.95,
      "f1": 0.9048,
      "support": 20
    },
    {
      "label": "biryani",
      "precision": 0.8571,
      "recall": 0.9,
      "f1": 0.878,
      "support": 20
    },
    {
      "label": "boondi",
      "precision": 0.9474,
      "recall": 0.9,
      "f1": 0.9231,
      "support": 20
    },
    {
      "label": "butter_chicken",
      "precision": 0.4419,
      "recall": 0.95,
      "f1": 0.6032,
      "support": 20
    },
    {
      "label": "chak_hao_kheer",
      "precision": 0.9474,
      "recall": 0.9,
      "f1": 0.9231,
      "support": 20
    },
    {
      "label": "cham_cham",
      "precision": 1.0,
      "recall": 0.4,
      "f1": 0.5714,
      "support": 20
    },
    {
      "label": "chana_masala",
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      "recall": 1.0,
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    {
      "label": "chapati",
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      "recall": 1.0,
      "f1": 0.8511,
      "support": 20
    },
    {
      "label": "chhena_kheeri",
      "precision": 0.0,
      "recall": 0.0,
      "f1": 0.0,
      "support": 20
    },
    {
      "label": "chicken_razala",
      "precision": 0.8,
      "recall": 1.0,
      "f1": 0.8889,
      "support": 20
    },
    {
      "label": "chicken_tikka",
      "precision": 0.9091,
      "recall": 0.5,
      "f1": 0.6452,
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    {
      "label": "chicken_tikka_masala",
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      "label": "chikki",
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      "label": "gulab_jamun",
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}