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README.md

@@ -1,339 +1,81 @@
 ---
 license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
 tags:
-- computer-vision
 - image-classification
-- vision
-base_model: google/vit-base-patch16-224-in21k
+- computer-vision
+- skincare
+- vision-transformer
 datasets:
-- 0xnu/skincare
+- custom
 metrics:
 - accuracy
 - f1
-model-index:
-- name: skincare-detection
-  results: []
-inference: true
-widget:
-- src: https://huggingface.co/0xnu/skincare-detection/resolve/main/joe.jpeg
-  example_title: Sample Skin Image
+pipeline_tag: image-classification
 ---
 
-## skincare-detection
+# skincare-detection
 
-A custom model for skincare image classification.
+## Model Description
 
-### Data Classes (Training)
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) for skincare image classification.
 
-The data classes are available [here](data_classes.txt).
+## Model Performance
 
-### Model Performance (Evaluation Results)
+- **Accuracy**: N/A
+- **F1 Score**: N/A
+- **Precision**: N/A
+- **Recall**: N/A
 
-```sh
-  eval_loss: 0.2097
-  eval_accuracy: 0.9779
-  eval_f1: 0.9778
-  eval_precision: 0.9794
-  eval_recall: 0.9779
-  eval_runtime: 14.2159
-  eval_samples_per_second: 19.1340
-  eval_steps_per_second: 0.6330
-  epoch: 12.0000
-```
+## Training Details
 
-### Confusion Matrix
+### Training Data
+Custom dataset with 3378 training samples
 
-![Confusion Matrix](confusion_matrix.png "Confusion Matrix")
+### Training Hyperparameters
 
-### Usage
+- Learning rate: 1e-05
+- Batch size: 1
+- Number of epochs: 1
+- Optimizer: AdamW
+- Scheduler: Linear with warmup
 
-```python
-#!/usr/bin/env python3
+### Classes
+Classes: acanthosis-nigricans, acne, acne-and-rosacea, acral-lentiginous-melanoma, acrodermatitis-enteropathica, alopecia-areata, alopecia-totalis, androgenetic-alopecia, aplasia-cutis, arsenicosis...
 
-import torch
+## Usage
+
+```python
 from transformers import ViTImageProcessor, ViTForImageClassification
 from PIL import Image
-import numpy as np
-from pathlib import Path
-import json
-from typing import List, Dict, Union
-import argparse
+import torch
+
+# Load model and processor
+processor = ViTImageProcessor.from_pretrained('0xnu/skincare-detection')
+model = ViTForImageClassification.from_pretrained('0xnu/skincare-detection')
 
-class SkincareClassifier:
-    """Skincare classifier with detailed outputs."""
-    
-    def __init__(self, model_name: str = '0xnu/skincare-detection'):
-        """Initialize the classifier with model loading."""
-        print(f"🔄 Loading model: {model_name}")
-        
-        try:
-            self.processor = ViTImageProcessor.from_pretrained(model_name)
-            self.model = ViTForImageClassification.from_pretrained(model_name)
-            self.model.eval()
-            
-            # Get class information
-            self.id2label = self.model.config.id2label
-            self.label2id = self.model.config.label2id
-            self.num_classes = len(self.id2label)
-            
-            print(f"✅ Model loaded successfully!")
-            print(f"📊 Classes ({self.num_classes}): {list(self.id2label.values())}")
-            
-        except Exception as e:
-            print(f"❌ Error loading model: {e}")
-            raise e
-    
-    def classify_single_image(self, image_path: Union[str, Path], 
-                            show_all_scores: bool = True,
-                            min_confidence: float = 0.01) -> Dict:
-        """
-        Classify a single image and return detailed results.
-        
-        Args:
-            image_path: Path to the image file
-            show_all_scores: Whether to show all class scores
-            min_confidence: Minimum confidence to display (0.0 to 1.0)
-            
-        Returns:
-            Dictionary with classification results
-        """
-        
-        try:
-            # Load and process image
-            image = Image.open(image_path).convert('RGB')
-            inputs = self.processor(images=image, return_tensors="pt")
-            
-            # Make prediction
-            with torch.no_grad():
-                outputs = self.model(**inputs)
-                logits = outputs.logits
-                probabilities = torch.softmax(logits, dim=-1)[0]
-            
-            # Get top prediction
-            predicted_class_id = logits.argmax().item()
-            predicted_label = self.id2label[predicted_class_id]
-            predicted_confidence = float(probabilities[predicted_class_id])
-            
-            # Prepare all scores
-            all_scores = {}
-            for class_id, class_name in self.id2label.items():
-                confidence = float(probabilities[class_id])
-                if confidence >= min_confidence:
-                    all_scores[class_name] = confidence
-            
-            # Sort by confidence
-            sorted_scores = dict(sorted(all_scores.items(), 
-                                     key=lambda x: x[1], reverse=True))
-            
-            result = {
-                'image_path': str(image_path),
-                'image_name': Path(image_path).name,
-                'predicted_class': predicted_label,
-                'predicted_confidence': predicted_confidence,
-                'all_scores': sorted_scores if show_all_scores else None,
-                'image_size': image.size,
-                'num_classes': self.num_classes
-            }
-            
-            return result
-            
-        except Exception as e:
-            return {
-                'image_path': str(image_path),
-                'error': str(e)
-            }
-    
-    def classify_multiple_images(self, image_paths: List[Union[str, Path]], 
-                               **kwargs) -> List[Dict]:
-        """Classify multiple images and return results."""
-        
-        results = []
-        total_images = len(image_paths)
-        
-        print(f"🔄 Processing {total_images} images...")
-        
-        for i, image_path in enumerate(image_paths, 1):
-            print(f"  Processing {i}/{total_images}: {Path(image_path).name}")
-            result = self.classify_single_image(image_path, **kwargs)
-            results.append(result)
-        
-        return results
-    
-    def classify_directory(self, directory_path: Union[str, Path], 
-                         **kwargs) -> List[Dict]:
-        """Classify all images in a directory."""
-        
-        directory_path = Path(directory_path)
-        
-        # Find all image files
-        image_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp'}
-        image_paths = [
-            p for p in directory_path.rglob('*') 
-            if p.suffix.lower() in image_extensions
-        ]
-        
-        if not image_paths:
-            print(f"❌ No images found in {directory_path}")
-            return []
-        
-        print(f"📁 Found {len(image_paths)} images in {directory_path}")
-        return self.classify_multiple_images(image_paths, **kwargs)
-    
-    def print_results(self, results: Union[Dict, List[Dict]], 
-                     detailed: bool = True):
-        """Print classification results in a nice format."""
-        
-        if isinstance(results, dict):
-            results = [results]
-        
-        print(f"\n🔍 CLASSIFICATION RESULTS")
-        print("=" * 60)
-        
-        successful_results = [r for r in results if 'error' not in r]
-        failed_results = [r for r in results if 'error' in r]
-        
-        # Print successful classifications
-        for i, result in enumerate(successful_results, 1):
-            print(f"\n📸 Image {i}: {result['image_name']}")
-            print(f"   Size: {result['image_size'][0]}x{result['image_size'][1]} pixels")
-            
-            # Top prediction
-            pred_class = result['predicted_class']
-            pred_conf = result['predicted_confidence']
-            print(f"\n🎯 TOP PREDICTION:")
-            print(f"   {pred_class.upper()}: {pred_conf:.1%}")
-            
-            # All scores if detailed
-            if detailed and result.get('all_scores'):
-                print(f"\n📊 ALL SCORES:")
-                for class_name, confidence in result['all_scores'].items():
-                    # Create progress bar
-                    bar_length = int(confidence * 40)  # Scale to 40 chars
-                    bar = "█" * bar_length + "░" * (40 - bar_length)
-                    
-                    print(f"   {class_name:>8}: {confidence:.1%} {bar}")
-            
-            print(f"   {'-' * 50}")
-        
-        # Print failed classifications
-        if failed_results:
-            print(f"\n❌ FAILED CLASSIFICATIONS ({len(failed_results)}):")
-            for result in failed_results:
-                print(f"   {result['image_path']}: {result['error']}")
-        
-        # Summary
-        if len(successful_results) > 1:
-            print(f"\n📈 SUMMARY:")
-            print(f"   Successfully processed: {len(successful_results)} images")
-            print(f"   Failed: {len(failed_results)} images")
-            
-            # Class distribution
-            class_counts = {}
-            for result in successful_results:
-                pred_class = result['predicted_class']
-                class_counts[pred_class] = class_counts.get(pred_class, 0) + 1
-            
-            print(f"\n📊 CLASS DISTRIBUTION:")
-            for class_name, count in sorted(class_counts.items()):
-                percentage = (count / len(successful_results)) * 100
-                print(f"   {class_name:>8}: {count:>3} images ({percentage:.1f}%)")
-    
-    def save_results(self, results: List[Dict], output_file: str):
-        """Save results to JSON file."""
-        
-        try:
-            with open(output_file, 'w') as f:
-                json.dump(results, f, indent=2, default=str)
-            print(f"💾 Results saved to: {output_file}")
-        except Exception as e:
-            print(f"❌ Error saving results: {e}")
+# Process image
+image = Image.open('path_to_your_image.jpg')
+inputs = processor(images=image, return_tensors="pt")
 
-def main():
-    """Main function with command line interface."""
-    
-    parser = argparse.ArgumentParser(description="Skincare Image Classification")
-    parser.add_argument('input', help='Image file or directory path')
-    parser.add_argument('--model', default='0xnu/skincare-detection',
-                       help='HuggingFace model name')
-    parser.add_argument('--output', help='Output JSON file for results')
-    parser.add_argument('--min-confidence', type=float, default=0.01,
-                       help='Minimum confidence to display (0.0-1.0)')
-    parser.add_argument('--brief', action='store_true',
-                       help='Show only top prediction')
-    parser.add_argument('--batch-size', type=int, default=1,
-                       help='Batch size for processing (not implemented yet)')
-    
-    args = parser.parse_args()
-    
-    try:
-        # Initialize classifier
-        classifier = SkincareClassifier(args.model)
-        
-        input_path = Path(args.input)
-        
-        if input_path.is_file():
-            # Single image
-            result = classifier.classify_single_image(
-                input_path,
-                show_all_scores=not args.brief,
-                min_confidence=args.min_confidence
-            )
-            classifier.print_results(result, detailed=not args.brief)
-            
-            if args.output:
-                classifier.save_results([result], args.output)
-        
-        elif input_path.is_dir():
-            # Directory of images
-            results = classifier.classify_directory(
-                input_path,
-                show_all_scores=not args.brief,
-                min_confidence=args.min_confidence
-            )
-            classifier.print_results(results, detailed=not args.brief)
-            
-            if args.output:
-                classifier.save_results(results, args.output)
-        
-        else:
-            print(f"❌ Error: {input_path} is not a valid file or directory")
-    
-    except Exception as e:
-        print(f"❌ Error: {e}")
+# Make prediction
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_id = logits.argmax().item()
+    predicted_label = model.config.id2label[predicted_class_id]
 
-if __name__ == "__main__":
-    # If run directly, you can also use it programmatically
-    import sys
-    
-    if len(sys.argv) == 1:
-        print("🔬 Skincare Classification")
-        print("=" * 40)
-        
-        # Initialize classifier
-        classifier = SkincareClassifier('0xnu/skincare-detection')
-        
-        # Example with your image
-        image_path = 'joe.jpeg'  # Your image
-        
-        if Path(image_path).exists():
-            result = classifier.classify_single_image(image_path)
-            classifier.print_results(result)
-        else:
-            print(f"❌ Image not found: {image_path}")
-            print("\n💡 Usage examples:")
-            print("python skincare.py joe.jpeg")
-            print("python skincare.py image_directory/")
-            print("python skincare.py joe.jpeg --output results.json")
-    else:
-        main()
+print(f"Predicted class: {predicted_label}")
 ```
 
-### Limitations and Bias
+## Limitations and Bias
 
 - The model performance depends on the quality and diversity of training data
 - May not generalise well to skincare images significantly different from training distribution
 - Evaluate model performance on your specific use case before deployment
 
-### Copyright
+## Training Environment
 
-(c) Copyright 2025 Finbarrs Oketunji. All Rights Reserved.
+- Framework: Transformers
+- PyTorch: 2.8.0
+- Hardware: cpu

checkpoint-53/config.json

@@ -0,0 +1,347 @@
+{
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "acanthosis-nigricans",
+    "1": "acne",
+    "2": "acne-and-rosacea",
+    "3": "acral-lentiginous-melanoma",
+    "4": "acrodermatitis-enteropathica",
+    "5": "alopecia-areata",
+    "6": "alopecia-totalis",
+    "7": "androgenetic-alopecia",
+    "8": "aplasia-cutis",
+    "9": "arsenicosis",
+    "10": "athlete-foot",
+    "11": "becker-nevus",
+    "12": "behcets-disease",
+    "13": "bowens",
+    "14": "calcinosis-cutis",
+    "15": "candidiasis",
+    "16": "cellulitis",
+    "17": "cellulitis-impetigo",
+    "18": "cheilitis",
+    "19": "chickenpox",
+    "20": "chromoblastomycosis",
+    "21": "cutaneous-larva-migrans",
+    "22": "dariers-disease",
+    "23": "dermatomyositis",
+    "24": "discoid-lupus-erythematosus",
+    "25": "drug-eruptions",
+    "26": "dyshidrotic-eczema",
+    "27": "ecthyma",
+    "28": "eczema",
+    "29": "ehlers-danlos-syndrome",
+    "30": "epidermal-nevus",
+    "31": "epidermolysis-bullosa",
+    "32": "epidermolysis-bullosa-pruriginosa",
+    "33": "epidermolytic-hyperkeratosis",
+    "34": "erythema-annulare-centrifigum",
+    "35": "erythema-elevatum-diutinum",
+    "36": "erythema-multiforme",
+    "37": "erythema-nodosum",
+    "38": "exanthems-and-drug-eruptions",
+    "39": "factitial-dermatitis",
+    "40": "fixed-eruptions",
+    "41": "folliculitis",
+    "42": "fordyce-spots",
+    "43": "granuloma-annulare",
+    "44": "granulomatous-diseases",
+    "45": "hair-loss-alopecia",
+    "46": "hair-loss-photos-alopecia-and-other-hair-diseases",
+    "47": "halo-nevus",
+    "48": "hemangioma",
+    "49": "herpes",
+    "50": "herpes-hpv-and-other-stds",
+    "51": "herpes-simplex",
+    "52": "herpes-zoster",
+    "53": "hidradenitis",
+    "54": "hypertrophic-lichen-planus",
+    "55": "ichthyosis",
+    "56": "impetigo",
+    "57": "impetigo-contagiosa",
+    "58": "incontinentia-pigmenti",
+    "59": "infectious-erythema",
+    "60": "infestations-and-bites",
+    "61": "juvenile-xanthogranuloma",
+    "62": "kaposi-sarcoma",
+    "63": "keloid",
+    "64": "keratoderma",
+    "65": "keratosis-pilaris",
+    "66": "langerhans-cell-histiocytosis",
+    "67": "lentigo-maligna",
+    "68": "lichen-amyloidosis",
+    "69": "lichen-simplex",
+    "70": "linear-scleroderma",
+    "71": "livedo-reticularis",
+    "72": "lupus-and-other-connective-tissue-diseases",
+    "73": "lupus-vulgaris",
+    "74": "lymphangioma",
+    "75": "malignant-acanthosis-nigricans",
+    "76": "malignant-melanoma",
+    "77": "measles",
+    "78": "melanoacanthoma",
+    "79": "melanoma-skin-cancer-nevi-and-moles",
+    "80": "milia",
+    "81": "moles",
+    "82": "molluscum-contagiosum",
+    "83": "monkeypox",
+    "84": "mucinosis",
+    "85": "mucous-cyst",
+    "86": "mycosis-fungoides",
+    "87": "nail-fungus",
+    "88": "necrobiosis-lipoidica",
+    "89": "neurodermatitis",
+    "90": "neurofibromatosis",
+    "91": "neurotic-excoriations",
+    "92": "neutrophilic-dermatoses",
+    "93": "nevus-of-ota",
+    "94": "nevus-sebaceus",
+    "95": "nevus-spilus",
+    "96": "normal",
+    "97": "oral-lichen-planus",
+    "98": "pagets",
+    "99": "papilomatosis-confluentes-and-reticulate",
+    "100": "pediculosis-capitis",
+    "101": "pemphigus-vulgaris",
+    "102": "perioral-dermatitis",
+    "103": "photodermatoses",
+    "104": "pilar-cyst",
+    "105": "pilomatricoma",
+    "106": "pityriasis-lichenoides-chronica",
+    "107": "pityriasis-rosea",
+    "108": "pityriasis-rubra-pilaris",
+    "109": "pityriasis-versicolor",
+    "110": "poison-ivy-photos-and-other-contact-dermatitis",
+    "111": "porokeratosis-actinic",
+    "112": "porphyria",
+    "113": "port-wine-stain",
+    "114": "prurigo-nodularis",
+    "115": "psoriasis-pictures-lichen-planus-and-related-diseases",
+    "116": "pyogenic-granuloma",
+    "117": "rhinophyma",
+    "118": "ringworm",
+    "119": "rosacea",
+    "120": "sarcoidosis",
+    "121": "scabies",
+    "122": "scabies-lyme-disease-and-other-infestations-and-bites",
+    "123": "scleroderma",
+    "124": "scleromyxedema",
+    "125": "seborrheic-dermatitis",
+    "126": "seborrheic-keratoses",
+    "127": "seborrheic-keratosis",
+    "128": "shingles",
+    "129": "skin-allergy",
+    "130": "skin-cancer",
+    "131": "skin-warts",
+    "132": "solar-lentigo",
+    "133": "solitary-mastocytosis",
+    "134": "stasis-edema",
+    "135": "stevens-johnson-syndrome",
+    "136": "striae-distensae",
+    "137": "sun-damage",
+    "138": "syringoma",
+    "139": "systemic-disease",
+    "140": "systemic-lupus-erythematosus",
+    "141": "telangiectases",
+    "142": "tinea",
+    "143": "tinea-barbae",
+    "144": "tinea-corporis",
+    "145": "tinea-faciei",
+    "146": "tinea-nigra",
+    "147": "tinea-pedis",
+    "148": "tinea-versicolor",
+    "149": "trichoepithelioma",
+    "150": "tuberculosis-verrucosa-cutis",
+    "151": "tuberous-sclerosis",
+    "152": "tungiasis",
+    "153": "urticaria-hives",
+    "154": "varicella",
+    "155": "verruca",
+    "156": "warts",
+    "157": "xanthomas",
+    "158": "xeroderma-pigmentosum"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "acanthosis-nigricans": 0,
+    "acne": 1,
+    "acne-and-rosacea": 2,
+    "acral-lentiginous-melanoma": 3,
+    "acrodermatitis-enteropathica": 4,
+    "alopecia-areata": 5,
+    "alopecia-totalis": 6,
+    "androgenetic-alopecia": 7,
+    "aplasia-cutis": 8,
+    "arsenicosis": 9,
+    "athlete-foot": 10,
+    "becker-nevus": 11,
+    "behcets-disease": 12,
+    "bowens": 13,
+    "calcinosis-cutis": 14,
+    "candidiasis": 15,
+    "cellulitis": 16,
+    "cellulitis-impetigo": 17,
+    "cheilitis": 18,
+    "chickenpox": 19,
+    "chromoblastomycosis": 20,
+    "cutaneous-larva-migrans": 21,
+    "dariers-disease": 22,
+    "dermatomyositis": 23,
+    "discoid-lupus-erythematosus": 24,
+    "drug-eruptions": 25,
+    "dyshidrotic-eczema": 26,
+    "ecthyma": 27,
+    "eczema": 28,
+    "ehlers-danlos-syndrome": 29,
+    "epidermal-nevus": 30,
+    "epidermolysis-bullosa": 31,
+    "epidermolysis-bullosa-pruriginosa": 32,
+    "epidermolytic-hyperkeratosis": 33,
+    "erythema-annulare-centrifigum": 34,
+    "erythema-elevatum-diutinum": 35,
+    "erythema-multiforme": 36,
+    "erythema-nodosum": 37,
+    "exanthems-and-drug-eruptions": 38,
+    "factitial-dermatitis": 39,
+    "fixed-eruptions": 40,
+    "folliculitis": 41,
+    "fordyce-spots": 42,
+    "granuloma-annulare": 43,
+    "granulomatous-diseases": 44,
+    "hair-loss-alopecia": 45,
+    "hair-loss-photos-alopecia-and-other-hair-diseases": 46,
+    "halo-nevus": 47,
+    "hemangioma": 48,
+    "herpes": 49,
+    "herpes-hpv-and-other-stds": 50,
+    "herpes-simplex": 51,
+    "herpes-zoster": 52,
+    "hidradenitis": 53,
+    "hypertrophic-lichen-planus": 54,
+    "ichthyosis": 55,
+    "impetigo": 56,
+    "impetigo-contagiosa": 57,
+    "incontinentia-pigmenti": 58,
+    "infectious-erythema": 59,
+    "infestations-and-bites": 60,
+    "juvenile-xanthogranuloma": 61,
+    "kaposi-sarcoma": 62,
+    "keloid": 63,
+    "keratoderma": 64,
+    "keratosis-pilaris": 65,
+    "langerhans-cell-histiocytosis": 66,
+    "lentigo-maligna": 67,
+    "lichen-amyloidosis": 68,
+    "lichen-simplex": 69,
+    "linear-scleroderma": 70,
+    "livedo-reticularis": 71,
+    "lupus-and-other-connective-tissue-diseases": 72,
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