week1_image_classifier/README.md

# Install required packages
!pip install -q datasets transformers accelerate evaluate pillow

# Complete Food-101 Training - Single Cell

# 1. Setup
from google.colab import userdata
import os
import torch
from datasets import load_dataset
from transformers import (
    AutoImageProcessor,
    AutoModelForImageClassification,
    TrainingArguments,
    Trainer
)
import evaluate
import numpy as np

# Setup HF token
hf_token = userdata.get('HF_TOKEN')
os.environ['HF_TOKEN'] = hf_token

# Check GPU
print(f"GPU Available: {torch.cuda.is_available()}")
print(f"GPU Name: {torch.cuda.get_device_name(0)}\n")

# 2. Load Dataset
print("Loading Food-101 dataset...")
dataset = load_dataset("food101")
print(f"Train samples: {len(dataset['train']):,}")
print(f"Test samples: {len(dataset['validation']):,}\n")

# Get class names
class_names = dataset['train'].features['label'].names
print(f"Number of classes: {len(class_names)}\n")

# 3. Setup Preprocessing
model_name = "microsoft/resnet-50"
processor = AutoImageProcessor.from_pretrained(model_name)

def preprocess_dataset(example):
    image = example['image'].convert("RGB")
    inputs = processor(image, return_tensors="pt")
    return {
        'pixel_values': inputs['pixel_values'].squeeze(0),
        'labels': example['label']
    }

# 4. Split and Preprocess Data
print("Splitting dataset...")
train_val = dataset['train'].train_test_split(test_size=0.1, seed=42)

print("Preprocessing train dataset (this takes 5-10 minutes)...")
train_dataset = train_val['train'].map(
    preprocess_dataset,
    remove_columns=['image', 'label'],
    batched=False
)

print("Preprocessing validation dataset...")
val_dataset = train_val['test'].map(
    preprocess_dataset,
    remove_columns=['image', 'label'],
    batched=False
)

print("Preprocessing test dataset...")
test_dataset = dataset['validation'].map(
    preprocess_dataset,
    remove_columns=['image', 'label'],
    batched=False
)

# Set format
train_dataset.set_format(type='torch', columns=['pixel_values', 'labels'])
val_dataset.set_format(type='torch', columns=['pixel_values', 'labels'])
test_dataset.set_format(type='torch', columns=['pixel_values', 'labels'])

print(f"\nTrain: {len(train_dataset):,}")
print(f"Validation: {len(val_dataset):,}")
print(f"Test: {len(test_dataset):,}\n")

# 5. Load Model
print("Loading model...")
id2label = {i: label for i, label in enumerate(class_names)}
label2id = {label: i for i, label in enumerate(class_names)}

model = AutoModelForImageClassification.from_pretrained(
    model_name,
    num_labels=len(class_names),
    id2label=id2label,
    label2id=label2id,
    ignore_mismatched_sizes=True
)

print(f"Model loaded: {model_name}")
print(f"Total parameters: {sum(p.numel() for p in model.parameters()):,}\n")

# 6. Setup Metrics
accuracy_metric = evaluate.load("accuracy")
f1_metric = evaluate.load("f1")

def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = np.argmax(predictions, axis=1)
    
    accuracy = accuracy_metric.compute(predictions=predictions, references=labels)
    f1 = f1_metric.compute(predictions=predictions, references=labels, average='weighted')
    
    return {
        'accuracy': accuracy['accuracy'],
        'f1': f1['f1']
    }

# 7. Training Arguments
training_args = TrainingArguments(
    output_dir="./food101-resnet50",
    num_train_epochs=5,
    per_device_train_batch_size=64,
    per_device_eval_batch_size=64,
    learning_rate=2e-5,
    weight_decay=0.01,
    warmup_steps=500,
    logging_steps=100,
    eval_strategy="epoch",
    save_strategy="epoch",
    save_total_limit=2,
    load_best_model_at_end=True,
    metric_for_best_model="accuracy",
    greater_is_better=True,
    fp16=True,
    dataloader_num_workers=0,
    push_to_hub=True,
    hub_model_id="suchithnj12/food101-resnet50",
    hub_strategy="end",
    report_to="none"
)

# 8. Create Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
    compute_metrics=compute_metrics
)

# 9. Train
print("Starting training (this takes 2-3 hours)...")
print("-" * 60)
train_result = trainer.train()

print("\nTraining completed!")
print(f"Final train loss: {train_result.metrics['train_loss']:.4f}")
print(f"Training time: {train_result.metrics['train_runtime']/60:.2f} minutes\n")

# 10. Evaluate on Test Set
print("Evaluating on test set...")
test_results = trainer.evaluate(test_dataset)

print("\nTest Results:")
print(f"Accuracy: {test_results['eval_accuracy']:.4f}")
print(f"F1 Score: {test_results['eval_f1']:.4f}\n")

# 11. Push to Hub
print("Pushing model to HuggingFace Hub...")
trainer.push_to_hub(commit_message="Food-101 ResNet-50 trained model")

print(f"\nModel available at: https://huggingface.co/suchithnj12/food101-resnet50")
print("Training pipeline completed successfully!")



ing builder script: 
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Downloading builder script: 
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Starting training (this takes 2-3 hours)...
------------------------------------------------------------
 [5330/5330 1:56:41, Epoch 5/5]
Epoch	Training Loss	Validation Loss	Accuracy	F1
1	4.496600	4.458635	0.166337	0.154736
2	3.814000	3.742320	0.298614	0.261986
3	3.257200	3.191649	0.362508	0.330396
4	2.935900	2.900200	0.397096	0.368665
5	2.847600	2.818526	0.408317	0.380199

Training completed!
Final train loss: 3.6109
Training time: 116.77 minutes

Evaluating on test set...
 [395/395 05:23]

Test Results:
Accuracy: 0.4450
F1 Score: 0.4139

Pushing model to HuggingFace Hub...
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Model available at: https://huggingface.co/suchithnj12/food101-resnet50
Training pipeline completed successfully!