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My Deep Learning Projects

Classification

  • Endoscopy Image Classification w/Swin Transformer (F1 Score: 0.87)
  • Crop Disease Classification w/Feature Fusion (EfficientNet, MobileNet) (F1 Score: 0.8)
  • 30 Plants Detection w/Custom ConvMixer based CNN (F1 Score: 0.77)
  • Bladder Tissue Classification w/ViT (F1 Score: 0.82)
  • Blood Cells Classification w/Custom ConvMixer based CNN (F1 Score: 0.98)
  • Brain Tumor Classification (Normal, Glioma, Meningioma, Pituitary) (Test ACC: 86.27 %)
  • Car Model and Color Multiclass Classification (F1 Score: 0.74)
  • Dental X-Rays Classification w/TPU (F1 Score: 0.72)
  • The Fashion Mnist Distributed DL Example (Mirrored Strategy) (Test ACC: 88.35 %)
  • Down Syndrome Detection w/CNN (Test AUC Score: 0.87)
  • Earthquake Events Classification (Mojor Event/Non-Major Event) (Test ACC: 64 %)
  • Fungus Detection w/10 Kfold CV Custom ConvMixer (F1 Score: 0.85)
  • Hieroglyph Multiclass Classification DenseNet (F1 Score: 0.85)
  • Higgs/Background Process Classification w/CNN using TPU (AUC Score: 0.83)
  • Jellyfish Classification (10KFold CV w/Custom ConvMixer) (F1 Score: 0.87)
  • MRI Sequence (T1, T2, T1 C+) Classification w/Custom CNN
  • Normal heartbeat/Myocardial Infarction Classification (ROC AUC Score: 0.842)
  • Pneumonia Detection w/Ensemble DL (Test AUC Score: 0.91)
  • Zipper Defect Classification (AUC Score: 0.98)
  • Glaucoma Classification w/ViT (F1 Score: 0.91)
  • Chest X-Ray Classification w/ViT (F1 Score: 0.9)
  • Document Classification w/ViT
  • (76 GB) 160 Polish Bird Sounds Classification
  • Rice Classification w/Custom ResNet50 (ACC 85%)
  • Maize Classification w/Custom ResNet18 (AUC Score: 0.98)
  • Sport Scene Classification w/ViT (3 KFold CV)
  • Segmentation

  • Brain tissue segmentation with U-net using TPU (Val Dice Coef: 0.88)
  • Brain tumor/anomaly segmentation with U-net using TPU
  • Asphalt Pavement Crack Segmentation U-Net
  • Eye Conjunctiva Segmentation with U-Net using TPU (Dice : 0.91, Jaccard : 0.82)
  • Iris Segmentation U-net w/TPU (Dice : 0.94, Jaccard : 0.88)
  • Particle Segmentation Custom DeepLabv3+ (Dice : 0.93, Jaccard : 0.88)
  • Retina Vessel Segmentation U-net w/TPU (Dice : 0.75)
  • Lung Segmentation UNet w/SeparableConv (Dice : 0.93)
  • Segmentation Medical Instrument w/Custom DeepLabv3+ (Dice : 0.86)
  • Tomato Segmentation w/detectron2 (mask AP: 61.94)
  • Brain Tumor Segmentation w/detectron2 (mAP@50:76.2)
  • Road Segmentation w/DeepLabv3+ from Scratch
  • Segmenting HuTu Cells DeepLabv3+ (Test Dice: 0.93)
  • Object Detection

  • Damaged Lamp Detection w/detectron2 (Faster R-CNN)
  • Plate Detection w/detectron2 (mAP@75: 89.19)
  • Tomato Detection w/detectron2 (mAP@50: 82.02)
  • Tiny Vehicle Detection w/detectron2 (mAP@50: 32.08)
  • Traffic Signs Detect w/detectron2 (mAP@50: 71.62)
  • Sign Detection w/Keras YOLO V8
  • Road Mark Detection (ResNet-50, ResNeXt 101 FPNs)
  • Bone Fracture Detection (ResNet-50, ResNeXt 101 FPNs)
  • Acne Detection w/Keras YOLO V8
  • Brain Tumor Detection w/Keras YOLO V8
  • Natural Language Processing

  • PaliGemma 3B for License Plate OCR
  • arXiv Articles Multi Label Classification w/FNet
  • IEEE Research Papers Topic Modelling w/LDA
  • Depressive vs Non-depressive Tweet w/Custom FNet (F1 Score: 0.88)
  • Yelp Review Stars Prediction (Classification) w/Gemma 7B (LoRA)
  • BBC News Topic Modeling w/LDA
  • Manufacturing Question-Answer w/Gemma 7B (Fine-Tuning LoRA) (Cosine Sim: 0.83)
  • Graph to Table w/Google's DePlot Model
  • Disease Article Topic Modelling w/BERTopic
  • Spam vs Ham Message w/ Gemma 7B Fine-Tuning (LoRA)
  • News Zero-Shot Topic Modelling w/BERTopic
  • Social Media Post Multiclass Classification w/DistilBERT
  • Gemma 2B Text Summarization w/Zero-Shot Prompting
  • Rating Prediction w/SentenceTransformer, CatBoost (MAE: 0.381)
  • Spam vs Ham Message Classification w/Custom FNet (F1 Score: 0.92)
  • Sentiment Analysis w/CatBoostClassifier (F1 Score: 0.703)
  • Complaint Analysis w/Ensemble Model (CatBoost, LR) (F1 Score: 0.86)
  • News Analysis w/Tensorflow (DistilBERT fine-tuning) (F1 Score: 0.89)
  • Emotion Classification w/LogisticRegression
  • Spam Mail Detection w/Tensorflow (DistilBERT fine-tuning) (F1 Score: 0.92)
  • Other Deep Learning Projects (Anomaly Detection, Image Captioning, Multiple Instance Learning etc.)

  • PaliGemma 3B for Image Captioning
  • Ford Motor Data Anomaly Detection with AutoEncoder
  • Satellite Image Captioning (ViT, Bi-LSTMs)
  • Molecule Activity, Deep Multiple Instance Learning
  • Cloud Classification (Involution Neural Network)
  • Car Detect w/Deep Multiple Instance Learning
  • Ford Motor Data Anomaly Detection with AutoEncoder
  • Satellite Image Captioning (ViT, Bi-LSTMs)
  • Molecule Activity, Deep Multiple Instance Learning
  • Cloud Classification (Involution Neural Network)
  • Car Detect w/Deep Multiple Instance Learning
  • Happy Detection w/Deep Multiple Instance Learning