Upload plant disease recognition model

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+model.keras filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+tags:
+- plants
+- plant-disease
+- image-classification
+- tensorflow
+- keras
+- resnet50
+license: apache-2.0
+pipeline_tag: image-classification
+---
+
+# plant-resnet50-38classes
+
+Modelo keras (tensorflow) para reconhecimento de plantas e doenças (38 classes).
+
+## 📋 Classes suportadas
+
+O modelo identifica as seguintes plantas e condições:
+
+- **Maçã**: sarna, podridão negra, ferrugem do cedro, saudável
+- **Mirtilo**: saudável
+- **Cereja**: oídio, saudável
+- **Milho**: mancha de cercospora, ferrugem comum, queima das folhas, saudável
+- **Uva**: podridão negra, esca, queima das folhas, saudável
+- **Laranja**: citrus greening
+- **Pêssego**: mancha bacteriana, saudável
+- **Pimentão**: mancha bacteriana, saudável
+- **Batata**: requeima precoce, requeima tardia, saudável
+- **Framboesa**: saudável
+- **Soja**: saudável
+- **Abóbora**: oídio
+- **Morango**: queima das folhas, saudável
+- **Tomate**: 10 condições diferentes
+
+Total: 38 classes
+
+## 🚀 Uso rápido
+
+```python
+import json
+import numpy as np
+from huggingface_hub import hf_hub_download
+import tensorflow as tf
+from PIL import Image
+
+# Baixa modelo e configuração
+repo_id = "raysarocha/plant-resnet50-38classes"
+cfg_path = hf_hub_download(repo_id, "config.json")
+model_path = hf_hub_download(repo_id, "model.keras")
+
+# Carrega configuração e modelo
+with open(cfg_path, "r") as f:
+    cfg = json.load(f)
+    
+model = tf.keras.models.load_model(model_path)
+
+# Processa imagem
+img = Image.open("sua_imagem.jpg").convert("RGB")
+img = img.resize((cfg["image_size"], cfg["image_size"]))
+arr = np.array(img).astype("float32") * cfg["rescale"]
+arr = np.expand_dims(arr, axis=0)
+
+# Predição
+predictions = model(arr)
+probs = tf.nn.softmax(predictions[0]).numpy()
+pred_idx = int(np.argmax(probs))
+confidence = float(probs[pred_idx])
+
+print(f"Classe: {cfg['id2label'][str(pred_idx)]}")
+print(f"Confiança: {confidence:.2%}")
+```
+
+## 📊 Informações do modelo
+
+- **Arquitetura**: resnet50
+- **Framework**: tensorflow/keras
+- **Entrada**: 224x224 pixels
+- **Normalização**: pixels / 255.0
+- **Classes**: 38
+
+## 📝 Licença
+
+Apache 2.0

config.json

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+{
+  "framework": "tf-keras",
+  "image_size": 224,
+  "id2label": {
+    "0": "Apple___Apple_scab",
+    "1": "Apple___Black_rot",
+    "2": "Apple___Cedar_apple_rust",
+    "3": "Apple___healthy",
+    "4": "Blueberry___healthy",
+    "5": "Cherry_(including_sour)___Powdery_mildew",
+    "6": "Cherry_(including_sour)___healthy",
+    "7": "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot",
+    "8": "Corn_(maize)___Common_rust_",
+    "9": "Corn_(maize)___Northern_Leaf_Blight",
+    "10": "Corn_(maize)___healthy",
+    "11": "Grape___Black_rot",
+    "12": "Grape___Esca_(Black_Measles)",
+    "13": "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)",
+    "14": "Grape___healthy",
+    "15": "Orange___Haunglongbing_(Citrus_greening)",
+    "16": "Peach___Bacterial_spot",
+    "17": "Peach___healthy",
+    "18": "Pepper,_bell___Bacterial_spot",
+    "19": "Pepper,_bell___healthy",
+    "20": "Potato___Early_blight",
+    "21": "Potato___Late_blight",
+    "22": "Potato___healthy",
+    "23": "Raspberry___healthy",
+    "24": "Soybean___healthy",
+    "25": "Squash___Powdery_mildew",
+    "26": "Strawberry___Leaf_scorch",
+    "27": "Strawberry___healthy",
+    "28": "Tomato___Bacterial_spot",
+    "29": "Tomato___Early_blight",
+    "30": "Tomato___Late_blight",
+    "31": "Tomato___Leaf_Mold",
+    "32": "Tomato___Septoria_leaf_spot",
+    "33": "Tomato___Spider_mites Two-spotted_spider_mite",
+    "34": "Tomato___Target_Spot",
+    "35": "Tomato___Tomato_Yellow_Leaf_Curl_Virus",
+    "36": "Tomato___Tomato_mosaic_virus",
+    "37": "Tomato___healthy"
+  },
+  "label2id": {
+    "Apple___Apple_scab": 0,
+    "Apple___Black_rot": 1,
+    "Apple___Cedar_apple_rust": 2,
+    "Apple___healthy": 3,
+    "Blueberry___healthy": 4,
+    "Cherry_(including_sour)___Powdery_mildew": 5,
+    "Cherry_(including_sour)___healthy": 6,
+    "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot": 7,
+    "Corn_(maize)___Common_rust_": 8,
+    "Corn_(maize)___Northern_Leaf_Blight": 9,
+    "Corn_(maize)___healthy": 10,
+    "Grape___Black_rot": 11,
+    "Grape___Esca_(Black_Measles)": 12,
+    "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)": 13,
+    "Grape___healthy": 14,
+    "Orange___Haunglongbing_(Citrus_greening)": 15,
+    "Peach___Bacterial_spot": 16,
+    "Peach___healthy": 17,
+    "Pepper,_bell___Bacterial_spot": 18,
+    "Pepper,_bell___healthy": 19,
+    "Potato___Early_blight": 20,
+    "Potato___Late_blight": 21,
+    "Potato___healthy": 22,
+    "Raspberry___healthy": 23,
+    "Soybean___healthy": 24,
+    "Squash___Powdery_mildew": 25,
+    "Strawberry___Leaf_scorch": 26,
+    "Strawberry___healthy": 27,
+    "Tomato___Bacterial_spot": 28,
+    "Tomato___Early_blight": 29,
+    "Tomato___Late_blight": 30,
+    "Tomato___Leaf_Mold": 31,
+    "Tomato___Septoria_leaf_spot": 32,
+    "Tomato___Spider_mites Two-spotted_spider_mite": 33,
+    "Tomato___Target_Spot": 34,
+    "Tomato___Tomato_Yellow_Leaf_Curl_Virus": 35,
+    "Tomato___Tomato_mosaic_virus": 36,
+    "Tomato___healthy": 37
+  },
+  "rescale": 0.00392156862745098,
+  "mean": null,
+  "std": null,
+  "num_classes": 38,
+  "architecture": "ResNet50",
+  "task": "image-classification"
+}

model.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:62c2303b88ea942e3511bfcc9cc2e3e80bf90b3bc91812f60b7a42379397d77a
+size 109397043