chore: add model and update readme

README.md

@@ -1,3 +1,107 @@
 ---
-license: mit
+license: apache-2.0
+tags:
+  - vision
+  - image-classification
+  - generated_from_trainer
+datasets:
+  - imagefolder
+model-index:
+  - name: vegetables-detector
+    results:
+      - task:
+          name: Image Classification
+          type: image-classification
+        dataset:
+          name: imagefolder
+          type: imagefolder
+          config: default
+          split: train
+          args: default
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 0.9721
+language:
+  - en
+pipeline_tag: image-classification
 ---
+
+# Vegetables Detector
+
+This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50).
+
+It achieves the following results on the evaluation set:
+
+- Loss: 0.0014
+- Accuracy: 0.9721
+
+## Model description
+
+This Model is a exploration test using the base model resnet-50 from microsoft.
+
+## Intended uses & limitations
+
+This Model was trained with a very small dataset
+[kritikseth/fruit-and-vegetable-image-recognition](https://www.kaggle.com/datasets/kritikseth/fruit-and-vegetable-image-recognition) and it was created to validate the resnet-50 fine-tuning capabilities
+
+### How to use
+
+Here is how to use this model to classify an image:
+
+```python
+import cv2
+import torch
+import torchvision.transforms as transforms
+from transformers import AutoModelForImageClassification
+from PIL import Image
+
+# Load the saved model and tokenizer
+model = AutoModelForImageClassification.from_pretrained("jazzmacedo/vegetables-detector")
+
+# Get the list of labels from the model's configuration
+labels = list(model.config.id2label.values())
+
+# Define the preprocessing transformation
+preprocess = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+image_path = "path/to/your/image.jpg"
+image = cv2.imread(image_path)
+image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+pil_image = Image.fromarray(image)  # Convert NumPy array to PIL image
+input_tensor = preprocess(pil_image).unsqueeze(0)
+
+# Run the image through the model
+outputs = model(input_tensor)
+
+# Get the predicted label index
+predicted_idx = torch.argmax(outputs.logits, dim=1).item()
+
+# Get the predicted label text
+predicted_label = labels[predicted_idx]
+
+# Print the predicted label
+print("Detected label:", predicted_label)
+```
+
+## Training and evaluation data
+
+Dataset Source: https://www.kaggle.com/datasets/kritikseth/fruit-and-vegetable-image-recognition
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+
+- learning_rate: 0.001
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10

config.json

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+{
+  "_name_or_path": "microsoft/resnet-50",
+  "architectures": [
+    "ResNetForImageClassification"
+  ],
+  "depths": [
+    3,
+    4,
+    6,
+    3
+  ],
+  "downsample_in_first_stage": false,
+  "embedding_size": 64,
+  "hidden_act": "relu",
+  "hidden_sizes": [
+    256,
+    512,
+    1024,
+    2048
+  ],
+  "id2label": {
+    "0": "apple",
+    "1": "banana",
+    "2": "beetroot",
+    "3": "bell pepper",
+    "4": "cabbage",
+    "5": "capsicum",
+    "6": "carrot",
+    "7": "cauliflower",
+    "8": "chilli pepper",
+    "9": "corn",
+    "10": "cucumber",
+    "11": "eggplant",
+    "12": "garlic",
+    "13": "ginger",
+    "14": "grapes",
+    "15": "jalepeno",
+    "16": "kiwi",
+    "17": "lemon",
+    "18": "lettuce",
+    "19": "mango",
+    "20": "onion",
+    "21": "orange",
+    "22": "paprika",
+    "23": "pear",
+    "24": "peas",
+    "25": "pineapple",
+    "26": "pomegranate",
+    "27": "potato",
+    "28": "raddish",
+    "29": "soy beans",
+    "30": "spinach",
+    "31": "sweetcorn",
+    "32": "sweetpotato",
+    "33": "tomato",
+    "34": "turnip",
+    "35": "watermelon"
+  },
+  "label2id": {
+    "apple": 0,
+    "banana": 1,
+    "beetroot": 2,
+    "bell pepper": 3,
+    "cabbage": 4,
+    "capsicum": 5,
+    "carrot": 6,
+    "cauliflower": 7,
+    "chilli pepper": 8,
+    "corn": 9,
+    "cucumber": 10,
+    "eggplant": 11,
+    "garlic": 12,
+    "ginger": 13,
+    "grapes": 14,
+    "jalepeno": 15,
+    "kiwi": 16,
+    "lemon": 17,
+    "lettuce": 18,
+    "mango": 19,
+    "onion": 20,
+    "orange": 21,
+    "paprika": 22,
+    "pear": 23,
+    "peas": 24,
+    "pineapple": 25,
+    "pomegranate": 26,
+    "potato": 27,
+    "raddish": 28,
+    "soy beans": 29,
+    "spinach": 30,
+    "sweetcorn": 31,
+    "sweetpotato": 32,
+    "tomato": 33,
+    "turnip": 34,
+    "watermelon": 35
+  },
+  "layer_type": "bottleneck",
+  "model_type": "resnet",
+  "num_channels": 3,
+  "out_features": null,
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4"
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.1"
+}

pytorch_model.bin

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