Create README.md

README.md

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+---
+language:
+- en
+base_model:
+- Ultralytics/YOLOv5
+- Ultralytics/YOLOv8
+- Ultralytics/YOLO11
+- microsoft/resnet-18
+- google/vit-base-patch16-224
+- microsoft/swin-tiny-patch4-window7-224
+- facebook/convnext-tiny-224
+pipeline_tag: image-classification
+---
+
+# HAM10000 Skin Lesion Classification
+
+<p align="center">
+  <a href="https://wandb.ai/nbamine-fsdm/ham10000-benchmarks">
+    <img src="https://img.shields.io/badge/Weights_&_Biases-FFBE00?style=for-the-badge&logo=WeightsAndBiases&logoColor=white" />
+  </a>
+  <a href="https://github.com/NBAmine/Vision-models-comparaison">
+    <img src="https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github&logoColor=white" />
+  </a>
+</p>
+
+## Model Description
+This model is a fine-tuned version of a computer vision architecture (YOLO, Transformer, or CNN) trained on the **HAM10000** dataset. The primary objective is the **binary classification** of skin lesions into two categories: **Benign** and **Malignant**. This work is part of a benchmark study evaluating modern architectures for dermatological triage.
+
+- **Task:** Binary Image Classification
+- **Dataset:** HAM10000 (Human Against Machine)
+- **Classes:** Benign, Malignant
+- **Developer:** NBAmine
+
+## Objective & Methodology
+The goal of this project is to identify the most effective architecture for early skin cancer detection. We evaluate several models including:
+- **YOLO Series:** v5n, v8n, 11n (Classification)
+- **Transformers:** ViT-B/16, Swin Tiny
+- **CNNs:** ResNet18, ConvNeXt Tiny
+
+### Training Highlights
+- **Input Resolution:** 224x224 pixels.
+- **Augmentation:** 180° rotations and flips to ensure rotation invariance.
+- **Class Balancing:** Strategic oversampling of the Malignant class to mitigate dataset imbalance and improve clinical reliability.
+
+## Evaluation Metrics
+The models are evaluated with a focus on sensitivity to ensure high detection rates for malignant cases:
+- **Primary Metrics:** Recall (Malignant class), Macro F1-Score.
+- **Secondary Metrics:** Accuracy, Precision, Validation Loss.
+
+## Intended Use
+This model is intended for **research and educational purposes only**. It is designed as a decision-support tool for medical imaging analysis and is **not** a replacement for professional clinical diagnosis by a dermatologist.