goyaljai/IITB-PML-SEM1

IITB PML Semester 1 — IPL Image Dataset

Dataset on Hugging Face: goyaljai/IITB-PML-SEM1

963 IPL cricket images, uniformly processed to 800 × 600 px JPEG, prepared for the Practical Machine Learning course, Semester 1, IIT Bombay.

Dataset Details

Property	Value
Total images	963
Format	JPEG
Dimensions	800 × 600 px (all uniform)
Size	~141 MB

Train / Test Split

Split	Folder	Count	%
Train	train/	674	70%
Test	test/	289	30%

Split is random with seed=42 for reproducibility.

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How to Load

from huggingface_hub import snapshot_download
from pathlib import Path

# Download full dataset
dataset_dir = Path(snapshot_download(repo_id="goyaljai/IITB-PML-SEM1", repo_type="dataset"))

# Train and test paths
train_dir = dataset_dir / "train"
test_dir  = dataset_dir / "test"

train_images = sorted(train_dir.glob("*.jpg"))
test_images  = sorted(test_dir.glob("*.jpg"))

print(f"Train: {len(train_images)} images")
print(f"Test : {len(test_images)} images")

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Example: K-Means Clustering on Train Set, Evaluate on Test Set

Cluster IPL images by colour histogram features. Fit KMeans on the train split, then assign test images to the nearest cluster.

from huggingface_hub import snapshot_download
from pathlib import Path
from PIL import Image
import numpy as np
from sklearn.cluster import KMeans
from sklearn.preprocessing import normalize
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# ── 1. Download dataset ──────────────────────────────────────────────────────
dataset_dir = Path(snapshot_download(repo_id="goyaljai/IITB-PML-SEM1", repo_type="dataset"))
train_images = sorted((dataset_dir / "train").glob("*.jpg"))
test_images  = sorted((dataset_dir / "test").glob("*.jpg"))
print(f"Train: {len(train_images)} | Test: {len(test_images)}")

# ── 2. Feature extraction (colour histogram) ─────────────────────────────────
def extract_histogram(path, bins=32):
    img = Image.open(path).convert("RGB")
    arr = np.array(img)
    hist = []
    for ch in range(3):
        h, _ = np.histogram(arr[:, :, ch], bins=bins, range=(0, 256))
        hist.extend(h)
    return np.array(hist, dtype=float)

print("Extracting train features...")
X_train = normalize(np.array([extract_histogram(p) for p in train_images]))

print("Extracting test features...")
X_test  = normalize(np.array([extract_histogram(p) for p in test_images]))

# ── 3. Fit KMeans on train ───────────────────────────────────────────────────
N_CLUSTERS = 8
kmeans = KMeans(n_clusters=N_CLUSTERS, random_state=42, n_init=10)
train_labels = kmeans.fit_predict(X_train)

print("\nTrain cluster distribution:")
for k in range(N_CLUSTERS):
    print(f"  Cluster {k}: {np.sum(train_labels == k)} images")

# ── 4. Predict on test ───────────────────────────────────────────────────────
test_labels = kmeans.predict(X_test)

print("\nTest cluster distribution:")
for k in range(N_CLUSTERS):
    print(f"  Cluster {k}: {np.sum(test_labels == k)} images")

# ── 5. Visualise 5 train samples + 2 test samples per cluster ───────────────
COLS = 7  # 5 train + 2 test
fig, axes = plt.subplots(N_CLUSTERS, COLS, figsize=(COLS * 3, N_CLUSTERS * 2.5))

for k in range(N_CLUSTERS):
    tr_paths = [p for p, l in zip(train_images, train_labels) if l == k][:5]
    te_paths = [p for p, l in zip(test_images,  test_labels)  if l == k][:2]
    row_paths = tr_paths + te_paths
    for j in range(COLS):
        ax = axes[k][j]
        if j < len(row_paths):
            ax.imshow(mpimg.imread(row_paths[j]))
            if j == 0:
                ax.set_title(f"Cluster {k}", fontsize=9)
            if j == 5:
                ax.set_title("TEST →", fontsize=8, color="orange")
        ax.axis("off")

plt.suptitle("KMeans Clusters  |  cols 1-5: train   cols 6-7: test", fontsize=11)
plt.tight_layout()
plt.savefig("kmeans_clusters.png", dpi=100)
plt.show()
print("Saved kmeans_clusters.png")

Tips

• Increase N_CLUSTERS (try 10–20) for finer groupings (team kits, ground types, crowd shots)
• Swap colour histograms for CNN embeddings (torchvision ResNet) for semantic clustering
• Use inertia_ and elbow method to pick the optimal K

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Requirements

pip install huggingface_hub pillow scikit-learn matplotlib numpy