Training in progress, step 500

README.md

@@ -12,21 +12,7 @@ metrics:
 - accuracy
 model-index:
 - name: vit-food-classification-gutkia01
-  results:
-    - task:
-        type: image-classification
-        name: Image Classification
-      dataset:
-        name: Custom Fruits vs. Junkfood
-        type: image
-        split: test
-      metrics:
-        - name: Accuracy
-          type: accuracy
-          value: 0.9998
-        - name: Loss
-          type: loss
-          value: 0.00047
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -34,53 +20,24 @@ should probably proofread and complete it, then remove this comment. -->
 
 # vit-food-classification-gutkia01
 
-This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the food-classification dataset.  
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the food-classification dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0008
-- Model Preparation Time: 0.0034
-- Accuracy: 0.9998
+- Loss: 0.0000
+- Accuracy: 1.0
 
-# 🍎 Fruits vs. Junkfood Classifier – Vision Transformer (gutkia01)
+## Model description
 
-This model is a fine-tuned version of [`google/vit-base-patch16-224`](https://huggingface.co/google/vit-base-patch16-224), trained on a custom binary dataset to distinguish between **healthy fruits** and **unhealthy fast food**.
+More information needed
 
-## 🧠 Model Description
+## Intended uses & limitations
 
-- **Architecture:** Vision Transformer (ViT)
-- **Base model:** `google/vit-base-patch16-224`
-- **Task:** Binary image classification: `Fruit` vs. `Junkfood`
-- **Framework:** Hugging Face Transformers Trainer
-- **Input format:** RGB images, 224×224, loaded via `imagefolder`
+More information needed
 
-## ✅ Intended Use & Limitations
+## Training and evaluation data
 
-### Appropriate Use Cases
+More information needed
 
-- Food classification in nutrition, health, or educational applications
-- Interactive demos comparing healthy vs. unhealthy food
-- Computer vision use cases with simple binary class structures
-
-### Limitations
-
-- Only supports binary classification (no subclass differentiation)
-- Cannot recognize new or abstract dishes (e.g. salad, sushi)
-- Cannot evaluate ingredients, calories, or portion sizes
-
-## 📊 Training and Evaluation Data
-
-The model was trained on a binary dataset composed of:
-
-- **Fruits360 Dataset**: 137,000+ structured fruit images in a controlled studio setup ([Kaggle link](https://www.kaggle.com/datasets/moltean/fruits))
-- **Fast Food Classification Dataset v2**: 20,000 fast food images, 10 categories (e.g., burger, pizza, fries) ([Kaggle link](https://www.kaggle.com/datasets/utkarshsaxenadn/fast-food-classification-dataset))
-
-### Dataset Composition
-
-The dataset is a combination of:
-
-- **Fruits360 Dataset** ([Kaggle](https://www.kaggle.com/datasets/moltean/fruits))  
-- **Fast Food Classification Dataset v2** ([Kaggle](https://www.kaggle.com/datasets/utkarshsaxenadn/fast-food-classification-dataset))
-
-## ⚙️ Training Procedure
+## Training procedure
 
 ### Training hyperparameters
 
@@ -95,21 +52,11 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Epoch | Training Loss | Validation Loss | Accuracy |
-|-------|---------------|------------------|----------|
-| 1     | 0.0000        | 0.0215           | 0.9975   |
-| 2     | 0.0000        | 0.00004          | 1.0000   |
-| 3     | 0.0000        | 0.00008          | 1.0000   |
-| 4     | 0.0000        | 0.00011          | 1.0000   |
-| 5     | 0.0000        | 0.00011          | 1.0000   |
-| 6     | 0.0000        | 0.00008          | 1.0000   |
 
-Final training loss: **0.00047**  
-Evaluation accuracy: **0.9998**
 
-## 🧰 Framework Versions
+### Framework versions
 
 - Transformers 4.52.3
 - Pytorch 2.6.0+cu124
-- Datasets 2.14.4
-- Tokenizers 0.21.1
+- Datasets 3.6.0
+- Tokenizers 0.21.1

model.safetensors

@@ -1,3 +1,3 @@
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