vit-base-patch16-224-in21k-leukemia

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vit-base-patch16-224-in21k-leukemia

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the Leukemia Dataset hosted on kaggle https://www.kaggle.com/datasets/andrewmvd/leukemia-classification. It achieves the following results on the evaluation set:

• Train Loss: 0.3256
• Train Accuracy: 0.8795
• Validation Loss: 0.6907
• Validation Accuracy: 0.6848
• Epoch: 13

Model description

Google Vision Transormer (ViT). fine-tuned on the white blood cancer - Leukemia - dataset

Intended uses & limitations

This model was fine-tuned as a part of my project LeukemiaAI, a fully integrated pipeline to detect Leukemia.

Github Repo: https://github.com/MohammedSaLah-Eldeen/LeukemiaAI

Training hyperparameters

• training_precision: mixed_float16
• optimizer: { 'inner_optimizer': { 'module': 'keras.optimizers.experimental', 'class_name': 'SGD', 'config': { 'name': 'SGD', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': 1, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': { 'module': 'keras.optimizers.schedules', 'class_name': 'CosineDecay', 'config': { 'initial_learning_rate': 0.001, 'decay_steps': 896, 'alpha': 0.0, 'name': None, 'warmup_target': None, 'warmup_steps': 0 }, 'registered_name': None }, 'momentum': 0.9, 'nesterov': False }, 'registered_name': None }, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000 }

Training results

Framework versions

• Transformers 4.35.0
• TensorFlow 2.13.0
• Datasets 2.1.0
• Tokenizers 0.14.1