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.gitattributes

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+checkpoint.pth filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: pytorch
+tags:
+- pytorch
+- image-classification
+- flowers
+- computer-vision
+pipeline_tag: image-classification
+---
+
+# 🌸 102-Flower Image Classifier — EfficientNet-B0
+
+PyTorch image-classification model trained to recognize **102 flower categories**.
+
+## Results
+
+- Architecture: **EfficientNet-B0**
+- Classes: **102**
+- Input: **224 × 224**
+- Best validation accuracy: **94.38%**
+- Epochs: **3**
+- Optimizer: **AdamW**
+- Learning rate: **0.001**
+
+## Files
+
+- `checkpoint.pth` — trained model checkpoint
+- `model_config.json` — model architecture metadata
+- `training_config.json` — training settings and validation result
+- `class_config.json` — exact class/index mappings
+- `labels.txt` — labels in model-output index order
+- `requirements.txt` — Python dependencies
+
+## Use the model
+
+```python
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from PIL import Image
+
+checkpoint = torch.load("checkpoint.pth", map_location="cpu")
+
+model = models.efficientnet_b0(weights=None)
+model.classifier[1] = nn.Linear(model.classifier[1].in_features, 102)
+
+model.load_state_dict(checkpoint["model_state_dict"])
+model.eval()
+
+idx_to_class = {
+    int(k): v for k, v in __import__("json").load(open("class_config.json"))["idx_to_class"].items()
+}
+
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],
+                         [0.229, 0.224, 0.225])
+])
+
+image = Image.open("flower.jpg").convert("RGB")
+x = transform(image).unsqueeze(0)
+
+with torch.no_grad():
+    probabilities = torch.softmax(model(x), dim=1)
+    confidence, prediction = probabilities.max(dim=1)
+
+idx = prediction.item()
+print("Prediction:", idx_to_class[idx])
+print("Confidence:", f"{confidence.item()*100:.2f}%")
+```
+
+## Checkpoint contents
+
+The checkpoint contains:
+- `epoch`
+- `model_state_dict`
+- `optimizer_state_dict`
+- `class_to_idx`
+
+## Training
+
+The model was trained with transfer learning using an ImageNet-pretrained EfficientNet-B0 backbone, then fine-tuned for the 102 flower classes.
+
+## Citation / attribution
+
+Please retain attribution to the model author when redistributing or building upon this model. Check the original dataset's license and terms before redistribution.
+
+## License
+
+Add the license that applies to your model and dataset before publishing.

checkpoint.pth

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+size 50127726

class_config.json

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+{
+  "class_to_idx": {
+    "1": 0,
+    "10": 1,
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+    "96": 98,
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+  },
+  "idx_to_class": {
+    "0": "pink primrose",
+    "1": "globe thistle",
+    "2": "blanket flower",
+    "3": "trumpet creeper",
+    "4": "blackberry lily",
+    "5": "snapdragon",
+    "6": "colt's foot",
+    "7": "king protea",
+    "8": "spear thistle",
+    "9": "yellow iris",
+    "10": "globe-flower",
+    "11": "purple coneflower",
+    "12": "peruvian lily",
+    "13": "balloon flower",
+    "14": "hard-leaved pocket orchid",
+    "15": "giant white arum lily",
+    "16": "fire lily",
+    "17": "pincushion flower",
+    "18": "fritillary",
+    "19": "red ginger",
+    "20": "grape hyacinth",
+    "21": "corn poppy",
+    "22": "prince of wales feathers",
+    "23": "stemless gentian",
+    "24": "artichoke",
+    "25": "canterbury bells",
+    "26": "sweet william",
+    "27": "carnation",
+    "28": "garden phlox",
+    "29": "love in the mist",
+    "30": "mexican aster",
+    "31": "alpine sea holly",
+    "32": "ruby-lipped cattleya",
+    "33": "cape flower",
+    "34": "great masterwort",
+    "35": "siam tulip",
+    "36": "sweet pea",
+    "37": "lenten rose",
+    "38": "barbeton daisy",
+    "39": "daffodil",
+    "40": "sword lily",
+    "41": "poinsettia",
+    "42": "bolero deep blue",
+    "43": "wallflower",
+    "44": "marigold",
+    "45": "buttercup",
+    "46": "oxeye daisy",
+    "47": "english marigold",
+    "48": "common dandelion",
+    "49": "petunia",
+    "50": "wild pansy",
+    "51": "primula",
+    "52": "sunflower",
+    "53": "pelargonium",
+    "54": "bishop of llandaff",
+    "55": "gaura",
+    "56": "geranium",
+    "57": "orange dahlia",
+    "58": "tiger lily",
+    "59": "pink-yellow dahlia",
+    "60": "cautleya spicata",
+    "61": "japanese anemone",
+    "62": "black-eyed susan",
+    "63": "silverbush",
+    "64": "californian poppy",
+    "65": "osteospermum",
+    "66": "spring crocus",
+    "67": "bearded iris",
+    "68": "windflower",
+    "69": "moon orchid",
+    "70": "tree poppy",
+    "71": "gazania",
+    "72": "azalea",
+    "73": "water lily",
+    "74": "rose",
+    "75": "thorn apple",
+    "76": "morning glory",
+    "77": "passion flower",
+    "78": "lotus lotus",
+    "79": "toad lily",
+    "80": "bird of paradise",
+    "81": "anthurium",
+    "82": "frangipani",
+    "83": "clematis",
+    "84": "hibiscus",
+    "85": "columbine",
+    "86": "desert-rose",
+    "87": "tree mallow",
+    "88": "magnolia",
+    "89": "cyclamen",
+    "90": "watercress",
+    "91": "monkshood",
+    "92": "canna lily",
+    "93": "hippeastrum",
+    "94": "bee balm",
+    "95": "ball moss",
+    "96": "foxglove",
+    "97": "bougainvillea",
+    "98": "camellia",
+    "99": "mallow",
+    "100": "mexican petunia",
+    "101": "bromelia"
+  }
+}

labels.txt

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+pink primrose
+globe thistle
+blanket flower
+trumpet creeper
+blackberry lily
+snapdragon
+colt's foot
+king protea
+spear thistle
+yellow iris
+globe-flower
+purple coneflower
+peruvian lily
+balloon flower
+hard-leaved pocket orchid
+giant white arum lily
+fire lily
+pincushion flower
+fritillary
+red ginger
+grape hyacinth
+corn poppy
+prince of wales feathers
+stemless gentian
+artichoke
+canterbury bells
+sweet william
+carnation
+garden phlox
+love in the mist
+mexican aster
+alpine sea holly
+ruby-lipped cattleya
+cape flower
+great masterwort
+siam tulip
+sweet pea
+lenten rose
+barbeton daisy
+daffodil
+sword lily
+poinsettia
+bolero deep blue
+wallflower
+marigold
+buttercup
+oxeye daisy
+english marigold
+common dandelion
+petunia
+wild pansy
+primula
+sunflower
+pelargonium
+bishop of llandaff
+gaura
+geranium
+orange dahlia
+tiger lily
+pink-yellow dahlia
+cautleya spicata
+japanese anemone
+black-eyed susan
+silverbush
+californian poppy
+osteospermum
+spring crocus
+bearded iris
+windflower
+moon orchid
+tree poppy
+gazania
+azalea
+water lily
+rose
+thorn apple
+morning glory
+passion flower
+lotus lotus
+toad lily
+bird of paradise
+anthurium
+frangipani
+clematis
+hibiscus
+columbine
+desert-rose
+tree mallow
+magnolia
+cyclamen
+watercress
+monkshood
+canna lily
+hippeastrum
+bee balm
+ball moss
+foxglove
+bougainvillea
+camellia
+mallow
+mexican petunia
+bromelia

model_config.json

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+{
+  "architecture": "efficientnet_b0",
+  "num_classes": 102,
+  "input_size": 224,
+  "pretrained_backbone": "ImageNet weights used during training",
+  "framework": "PyTorch",
+  "torchvision": true,
+  "checkpoint_format": "custom checkpoint with model_state_dict"
+}

requirements.txt

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+torch
+torchvision
+Pillow

training_config.json

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+{
+  "learning_rate": 0.001,
+  "hidden_units": 512,
+  "epochs": 3,
+  "batch_size": 32,
+  "optimizer": "AdamW",
+  "scheduler": {
+    "type": "StepLR",
+    "step_size": 5,
+    "gamma": 0.1
+  },
+  "loss": "CrossEntropyLoss",
+  "best_validation_accuracy_percent": 94.38,
+  "normalization": {
+    "mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "std": [
+      0.229,
+      0.224,
+      0.225
+    ]
+  },
+  "train_transforms": [
+    "RandomResizedCrop(224)",
+    "RandomHorizontalFlip",
+    "ColorJitter(brightness=0.2,contrast=0.2,saturation=0.2,hue=0.1)"
+  ],
+  "validation_transforms": [
+    "Resize(256)",
+    "CenterCrop(224)"
+  ]
+}