diff --git "a/CLIP_B32_finetune_cluster.ipynb" "b/CLIP_B32_finetune_cluster.ipynb"
--- "a/CLIP_B32_finetune_cluster.ipynb"
+++ "b/CLIP_B32_finetune_cluster.ipynb"
@@ -1 +1 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/CLIP_B32_finetune_cluster.ipynb","timestamp":1760890109028},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/CLIP_B32_finetune_cluster.ipynb","timestamp":1760880784010},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/CLIP_B32_finetune_cluster.ipynb","timestamp":1760509652530},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/CLIP_B32_finetune_cluster.ipynb","timestamp":1760371508137},{"file_id":"1wufnt5hqKHLuoX9wDdyzarENUjOO_s3N","timestamp":1760363981901},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/CLIP_cluster.ipynb","timestamp":1760363231133}],"gpuType":"T4","authorship_tag":"ABX9TyOP8KMhl1SIT4XG3AiDhbNj"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"G9yAxL_ViF7y"},"outputs":[],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"markdown","source":["Install Required Libraries\n","Run this cell to install the necessary packages. CLIP requires PyTorch, and we'll use scikit-learn for clustering, along with Pillow for image loading and matplotlib for visualization."],"metadata":{"id":"ji2qFha2icZi"}},{"cell_type":"code","source":["#@markdown Unzip training data from drive to /content/ (if required)\n","path = '/content/drive/MyDrive/training_data.zip' #@param {type:'string'}\n","\n","%cd /content/\n","!unzip  {path}"],"metadata":{"id":"59Tf9llpSGoz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!pip install ftfy regex tqdm\n","!pip install git+https://github.com/openai/CLIP.git\n","!pip install scikit-learn matplotlib pillow umap-learn  # UMAP is optional for 2D visualization"],"metadata":{"id":"WncaEzzGiaO2"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Load Images and Extract CLIP Embeddings\n","\n","Upload your images the normal way ( `/content/`) prior to running this cell.\n","\n","This code loads all images (supports JPG, PNG, etc.), preprocesses them, and extracts 512-dimensional embeddings using the ViT-B/32 CLIP model."],"metadata":{"id":"EnqyKHcOilVA"}},{"cell_type":"code","source":["!pip install open_clip_torch\n","\n","import os\n","import numpy as np\n","import torch\n","import open_clip\n","from PIL import Image\n","\n","# Configuration\n","image_dir = '/content/'  # Update this path\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n","model_name = \"ViT-B-32\"  # Available per error message\n","pretrained = \"laion400m_e32\"  # Robust pretrained weights\n","\n","# Load OpenCLIP model\n","model, _, preprocess = open_clip.create_model_and_transforms(model_name, pretrained=pretrained)\n","model.to(device)\n","model.eval()\n","\n","# Load images and extract embeddings\n","embeddings = []\n","image_paths = []\n","image_names = []\n","\n","for filename in os.listdir(image_dir):\n","    if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):\n","        img_path = os.path.join(image_dir, filename)\n","        try:\n","            image = preprocess(Image.open(img_path)).unsqueeze(0).to(device)\n","            with torch.no_grad():\n","                embedding = model.encode_image(image)\n","            embeddings.append(embedding.cpu().numpy().flatten())\n","            image_paths.append(img_path)\n","            image_names.append(filename)\n","            print(f\"Processed: {filename}\")\n","        except Exception as e:\n","            print(f\"Error processing {filename}: {e}\")\n","\n","embeddings = np.array(embeddings)\n","print(f\"Extracted embeddings for {len(embeddings)} images. Shape: {embeddings.shape}\")"],"metadata":{"id":"IcqN15af460q"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Perform Clustering\n","We'll use K-Means clustering on the embeddings. You can choose the number of clusters (`n_clusters`) based on your dataset size (e.g., try 5-10). We'll also compute the silhouette score to evaluate cluster quality (higher is better).\n","\n","For visualization, we'll optionally reduce dimensions to 2D using UMAP."],"metadata":{"id":"HQsc2r-ii6cK"}},{"cell_type":"code","source":["from umap import UMAP  # For 2D projection (optional)\n","import os\n","import numpy as np\n","import torch\n","import clip\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","from sklearn.cluster import KMeans\n","from sklearn.metrics import silhouette_score\n","import warnings\n","warnings.filterwarnings('ignore')\n","#@markdown Choose number of clusters (experiment with this)\n","n_clusters = 50  # @param {type:'slider' , min:1 , max:200, step:1}\n","\n","# Perform K-Means clustering\n","kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)\n","cluster_labels = kmeans.fit_predict(embeddings)\n","\n","# Evaluate clustering quality\n","sil_score = silhouette_score(embeddings, cluster_labels)\n","print(f\"Silhouette Score: {sil_score:.3f} (closer to 1 is better)\")\n","\n","# Optional: 2D visualization with UMAP\n","reducer = UMAP(random_state=42, n_components=2)\n","embed_2d = reducer.fit_transform(embeddings)\n","\n","plt.figure(figsize=(10, 8))\n","scatter = plt.scatter(embed_2d[:, 0], embed_2d[:, 1], c=cluster_labels, cmap='tab10', s=50)\n","plt.colorbar(scatter)\n","plt.title(f'2D UMAP Projection of CLIP Embeddings (K={n_clusters} Clusters)')\n","plt.xlabel('UMAP 1')\n","plt.ylabel('UMAP 2')\n","plt.show()"],"metadata":{"id":"WM9wug70jCtR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import shutil\n","import os\n","from PIL import Image\n","\n","# Create output directories\n","output_dir = '/content/clusters'  # Output base directory\n","os.makedirs(output_dir, exist_ok=True)\n","\n","move_files = False  # Set to True to move files, False to copy\n","\n","# Create directories for each cluster\n","for i in range(n_clusters):\n","    cluster_dir = os.path.join(output_dir, f'cluster_{i}')\n","    os.makedirs(cluster_dir, exist_ok=True)\n","\n","# Form inputs using Colab's # @param\n","output_format = \"JPEG\"  # @param [\"JPEG\", \"PNG\", \"WEBP\"]\n","quality = 100  # @param {type:\"slider\", min:0, max:100, step:1}\n","\n","# Function to convert and save images\n","for idx, label in enumerate(cluster_labels):\n","    src_path = image_paths[idx]  # Use full path\n","    # Create destination filename with selected extension\n","    dst_filename = os.path.splitext(image_names[idx])[0] + f'.{output_format.lower()}'\n","    dst_path = os.path.join(output_dir, f'cluster_{label}', dst_filename)\n","\n","    try:\n","        # Open and convert image\n","        with Image.open(src_path).convert('RGB') as img:\n","            if output_format == 'JPEG':\n","                img.save(dst_path, 'JPEG', quality=quality, optimize=True)\n","            elif output_format == 'PNG':\n","                # PNG compression: 0 (max compression) to 9 (no compression)\n","                # Map quality 0-100 to PNG compression 9-0\n","                png_compression = int(9 - (quality / 100 * 9))\n","                img.save(dst_path, 'PNG', compress_level=png_compression)\n","            elif output_format == 'WEBP':\n","                img.save(dst_path, 'WEBP', quality=quality)\n","\n","            if move_files:\n","                os.remove(src_path)  # Delete original if moving\n","            print(f\"Assigned {image_names[idx]} as {dst_filename} to cluster_{label}\")\n","    except Exception as e:\n","        print(f\"Error converting {image_names[idx]} to {output_format}: {e}\")\n","\n","print(f\"Images sorted into {n_clusters} clusters in '{output_dir}' as .{output_format.lower()}\")"],"metadata":{"id":"1fMT3PmCOSyh"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Sort Images into Clusters\n","This creates subdirectories for each cluster and moves/copies the images there. Set `move_files=True` to move (or False to copy)."],"metadata":{"id":"aWSOgPj5jLLI"}},{"cell_type":"code","source":["from PIL import Image\n","import matplotlib.pyplot as plt\n","import os\n","\n","def display_cluster_samples(cluster_dir, n_samples=3):\n","    # Updated to include .jpg, .png, and .webp files\n","    images = [f for f in os.listdir(cluster_dir) if f.lower().endswith(('.jpg', '.jpeg', '.png', '.webp'))][:n_samples]\n","    if not images:\n","        print(f\"No images in {cluster_dir}\")\n","        return\n","\n","    fig, axs = plt.subplots(1, len(images), figsize=(5 * len(images), 5))\n","    if len(images) == 1:\n","        axs = [axs]\n","    for j, img_file in enumerate(images):\n","        img_path = os.path.join(cluster_dir, img_file)\n","        try:\n","            img = Image.open(img_path).convert('RGB')  # Ensure RGB for display\n","            axs[j].imshow(img)\n","            axs[j].set_title(img_file)\n","            axs[j].axis('off')\n","        except Exception as e:\n","            print(f\"Error displaying {img_file}: {e}\")\n","    plt.show()\n","\n","# Display samples from each cluster\n","for i in range(n_clusters):\n","    cluster_dir = os.path.join(output_dir, f'cluster_{i}')\n","    print(f\"\\nSamples from Cluster {i}:\")\n","    display_cluster_samples(cluster_dir)"],"metadata":{"id":"ANg2q03kN6C_"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Visualize Sample Images per Cluster\n","Display a few sample images from each cluster to inspect the results."],"metadata":{"id":"Tg_q68KnjUb5"}},{"cell_type":"code","source":["#@markdown Upload to Google Drive as .zip folder (Be mindful of Google Drive Terms of Service)\n","drive_folder_name = 'clusters' # @param {type:'string'}\n","\n","%cd /content/\n","!zip -r /content/drive/MyDrive/{drive_folder_name}.zip {output_dir}\n","\n"],"metadata":{"id":"w2Gzortz0NuD"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/CLIP_B32_finetune_cluster.ipynb","timestamp":1761514713164},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/CLIP_B32_finetune_cluster.ipynb","timestamp":1760890109028},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/CLIP_B32_finetune_cluster.ipynb","timestamp":1760880784010},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/CLIP_B32_finetune_cluster.ipynb","timestamp":1760509652530},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/CLIP_B32_finetune_cluster.ipynb","timestamp":1760371508137},{"file_id":"1wufnt5hqKHLuoX9wDdyzarENUjOO_s3N","timestamp":1760363981901},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/CLIP_cluster.ipynb","timestamp":1760363231133}],"gpuType":"T4","authorship_tag":"ABX9TyOhqF+4zE/PirHLyeDXPaji"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"G9yAxL_ViF7y","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1761513814016,"user_tz":-60,"elapsed":18335,"user":{"displayName":"","userId":""}},"outputId":"9fcb3a77-1fc6-4586-9763-4a2ed7955525"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"markdown","source":["Install Required Libraries\n","Run this cell to install the necessary packages. CLIP requires PyTorch, and we'll use scikit-learn for clustering, along with Pillow for image loading and matplotlib for visualization."],"metadata":{"id":"ji2qFha2icZi"}},{"cell_type":"code","source":["#@markdown Unzip training data from drive to /content/ (if required)\n","path = '/content/drive/MyDrive/training_data_06593.zip' #@param {type:'string'}\n","\n","%cd /content/\n","!unzip  {path}"],"metadata":{"id":"59Tf9llpSGoz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!pip install ftfy regex tqdm\n","!pip install git+https://github.com/openai/CLIP.git\n","!pip install scikit-learn matplotlib pillow umap-learn  # UMAP is optional for 2D visualization"],"metadata":{"id":"WncaEzzGiaO2"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Load Images and Extract CLIP Embeddings\n","\n","Upload your images the normal way ( `/content/`) prior to running this cell.\n","\n","This code loads all images (supports JPG, PNG, etc.), preprocesses them, and extracts 512-dimensional embeddings using the ViT-B/32 CLIP model."],"metadata":{"id":"EnqyKHcOilVA"}},{"cell_type":"code","source":["!pip install open_clip_torch\n","\n","import os\n","import numpy as np\n","import torch\n","import open_clip\n","from PIL import Image\n","\n","# Configuration\n","image_dir = '/content/'  # Update this path\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n","model_name = \"ViT-B-32\"  # Available per error message\n","pretrained = \"laion400m_e32\"  # Robust pretrained weights\n","\n","# Load OpenCLIP model\n","model, _, preprocess = open_clip.create_model_and_transforms(model_name, pretrained=pretrained)\n","model.to(device)\n","model.eval()\n","\n","# Load images and extract embeddings\n","embeddings = []\n","image_paths = []\n","image_names = []\n","\n","# Recursively find images in subdirectories\n","for root, _, files in os.walk(image_dir):\n","    for filename in files:\n","        if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):\n","            img_path = os.path.join(root, filename)\n","            try:\n","                image = preprocess(Image.open(img_path)).unsqueeze(0).to(device)\n","                with torch.no_grad():\n","                    embedding = model.encode_image(image)\n","                embeddings.append(embedding.cpu().numpy().flatten())\n","                image_paths.append(img_path)\n","                image_names.append(filename)\n","                print(f\"Processed: {filename}\")\n","            except Exception as e:\n","                print(f\"Error processing {filename}: {e}\")\n","\n","embeddings = np.array(embeddings)\n","print(f\"Extracted embeddings for {len(embeddings)} images. Shape: {embeddings.shape}\")"],"metadata":{"id":"IcqN15af460q"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Perform Clustering\n","We'll use K-Means clustering on the embeddings. You can choose the number of clusters (`n_clusters`) based on your dataset size (e.g., try 5-10). We'll also compute the silhouette score to evaluate cluster quality (higher is better).\n","\n","For visualization, we'll optionally reduce dimensions to 2D using UMAP."],"metadata":{"id":"HQsc2r-ii6cK"}},{"cell_type":"code","source":["from umap import UMAP  # For 2D projection (optional)\n","import os\n","import numpy as np\n","import torch\n","import clip\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","from sklearn.cluster import KMeans\n","from sklearn.metrics import silhouette_score\n","import warnings\n","warnings.filterwarnings('ignore')\n","#@markdown Choose number of clusters (experiment with this)\n","n_clusters = 50  # @param {type:'slider' , min:1 , max:200, step:1}\n","\n","# Perform K-Means clustering\n","kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)\n","cluster_labels = kmeans.fit_predict(embeddings)\n","\n","# Evaluate clustering quality\n","sil_score = silhouette_score(embeddings, cluster_labels)\n","print(f\"Silhouette Score: {sil_score:.3f} (closer to 1 is better)\")\n","\n","# Optional: 2D visualization with UMAP\n","reducer = UMAP(random_state=42, n_components=2)\n","embed_2d = reducer.fit_transform(embeddings)\n","\n","plt.figure(figsize=(10, 8))\n","scatter = plt.scatter(embed_2d[:, 0], embed_2d[:, 1], c=cluster_labels, cmap='tab10', s=50)\n","plt.colorbar(scatter)\n","plt.title(f'2D UMAP Projection of CLIP Embeddings (K={n_clusters} Clusters)')\n","plt.xlabel('UMAP 1')\n","plt.ylabel('UMAP 2')\n","plt.show()"],"metadata":{"id":"WM9wug70jCtR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import shutil\n","import os\n","from PIL import Image\n","\n","# Create output directories\n","output_dir = '/content/clusters'  # Output base directory\n","os.makedirs(output_dir, exist_ok=True)\n","\n","move_files = False  # Set to True to move files, False to copy\n","\n","# Create directories for each cluster\n","for i in range(n_clusters):\n","    cluster_dir = os.path.join(output_dir, f'cluster_{i}')\n","    os.makedirs(cluster_dir, exist_ok=True)\n","\n","# Form inputs using Colab's # @param\n","output_format = \"JPEG\"  # @param [\"JPEG\", \"PNG\", \"WEBP\"]\n","quality = 100  # @param {type:\"slider\", min:0, max:100, step:1}\n","\n","# Function to convert and save images\n","for idx, label in enumerate(cluster_labels):\n","    src_path = image_paths[idx]  # Use full path\n","    # Create destination filename with selected extension\n","    dst_filename = os.path.splitext(image_names[idx])[0] + f'.{output_format.lower()}'\n","    dst_path = os.path.join(output_dir, f'cluster_{label}', dst_filename)\n","\n","    try:\n","        # Open and convert image\n","        with Image.open(src_path).convert('RGB') as img:\n","            if output_format == 'JPEG':\n","                img.save(dst_path, 'JPEG', quality=quality, optimize=True)\n","            elif output_format == 'PNG':\n","                # PNG compression: 0 (max compression) to 9 (no compression)\n","                # Map quality 0-100 to PNG compression 9-0\n","                png_compression = int(9 - (quality / 100 * 9))\n","                img.save(dst_path, 'PNG', compress_level=png_compression)\n","            elif output_format == 'WEBP':\n","                img.save(dst_path, 'WEBP', quality=quality)\n","\n","            if move_files:\n","                os.remove(src_path)  # Delete original if moving\n","            print(f\"Assigned {image_names[idx]} as {dst_filename} to cluster_{label}\")\n","    except Exception as e:\n","        print(f\"Error converting {image_names[idx]} to {output_format}: {e}\")\n","\n","print(f\"Images sorted into {n_clusters} clusters in '{output_dir}' as .{output_format.lower()}\")"],"metadata":{"id":"1fMT3PmCOSyh"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Sort Images into Clusters\n","This creates subdirectories for each cluster and moves/copies the images there. Set `move_files=True` to move (or False to copy)."],"metadata":{"id":"aWSOgPj5jLLI"}},{"cell_type":"code","source":["from PIL import Image\n","import matplotlib.pyplot as plt\n","import os\n","\n","def display_cluster_samples(cluster_dir, n_samples=3):\n","    # Updated to include .jpg, .png, and .webp files\n","    images = [f for f in os.listdir(cluster_dir) if f.lower().endswith(('.jpg', '.jpeg', '.png', '.webp'))][:n_samples]\n","    if not images:\n","        print(f\"No images in {cluster_dir}\")\n","        return\n","\n","    fig, axs = plt.subplots(1, len(images), figsize=(5 * len(images), 5))\n","    if len(images) == 1:\n","        axs = [axs]\n","    for j, img_file in enumerate(images):\n","        img_path = os.path.join(cluster_dir, img_file)\n","        try:\n","            img = Image.open(img_path).convert('RGB')  # Ensure RGB for display\n","            axs[j].imshow(img)\n","            axs[j].set_title(img_file)\n","            axs[j].axis('off')\n","        except Exception as e:\n","            print(f\"Error displaying {img_file}: {e}\")\n","    plt.show()\n","\n","# Create output directories\n","output_dir = '/content/clusters'  # Output base directory\n","\n","# Display samples from each cluster\n","for i in range(n_clusters):\n","    cluster_dir = os.path.join(output_dir, f'cluster_{i}')\n","    print(f\"\\nSamples from Cluster {i}:\")\n","    display_cluster_samples(cluster_dir)"],"metadata":{"id":"ANg2q03kN6C_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import shutil\n","\n","nums ='1,2,4,9,11,12,14,16,18,21,25,27,29,30,34,36,37,43,49'#@param {type:'string'}\n","\n","for num in nums.split(','):\n","  shutil.rmtree(f'/content/clusters/cluster_{num.strip()}')\n","\n","\n","\n","\n","\n","\n"],"metadata":{"id":"zKKETYPHbvQ2"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Visualize Sample Images per Cluster\n","Display a few sample images from each cluster to inspect the results."],"metadata":{"id":"Tg_q68KnjUb5"}},{"cell_type":"code","source":["#@markdown Upload to Google Drive as .zip folder (Be mindful of Google Drive Terms of Service)\n","drive_folder_name = 'clusters' # @param {type:'string'}\n","\n","%cd /content/\n","!zip -r /content/drive/MyDrive/{drive_folder_name}.zip {output_dir}\n","\n"],"metadata":{"id":"w2Gzortz0NuD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1761514681455,"user_tz":-60,"elapsed":72552,"user":{"displayName":"","userId":""}},"outputId":"f4b9fa26-1471-47af-c3b2-9bde5dfe32f8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content\n","  adding: content/clusters/ (stored 0%)\n","  adding: content/clusters/cluster_44/ (stored 0%)\n","  adding: content/clusters/cluster_44/panel_17_2.jpeg (deflated 0%)\n","  adding: content/clusters/cluster_44/panel_7_0.jpeg (deflated 0%)\n","  adding: content/clusters/cluster_44/panel_11_2.jpeg (deflated 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