القائمة
Generate descriptive text prompts from images by combining BLIP captioning with CLIP cosine similarity against curated label banks for medium, movement, and flavor attributes
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Memory-efficient Keras generator that streams sharded CSV files in chunks, renders strokes to images on-the-fly, and yields batches for training on datasets too large for memory
Stack 1D convolutions for local feature extraction before bidirectional LSTMs to classify variable-length stroke sequences into hundreds of doodle categories
Partition a large dataset into N balanced shards using integer key modulo arithmetic for reproducible, class-interleaved splits across CSV files
Normalize raw stroke coordinates to 0-255 range, resample at uniform arc-length spacing, then apply Ramer-Douglas-Peucker simplification
Render stroke sequences to grayscale images with temporal intensity encoding where earlier strokes are brighter and later strokes fade to encode drawing order
Score each face in a video as anomalous by computing L2 distance from the embedding centroid of all faces, then convert to probability via logistic function
| name | cv-clip-interrogator-captioning |
| description | Generate descriptive text prompts from images by combining BLIP captioning with CLIP cosine similarity against curated label banks for medium, movement, and flavor attributes |
When you need to reverse-engineer or describe an image as a text prompt (e.g., for image-to-prompt tasks), CLIP Interrogator combines two models: BLIP generates a base caption, then CLIP matches the image embedding against precomputed text embeddings from curated label banks (mediums, movements, flavors). The top-matching labels are appended to the caption, producing a rich prompt that captures style, medium, and content.
import torch
from clip_interrogator import Config, Interrogator
ci = Interrogator(Config(clip_model_name="ViT-L-14/openai"))
cos = torch.nn.CosineSimilarity(dim=1)
mediums_features = torch.stack([torch.from_numpy(t) for t in ci.mediums.embeds]).to(ci.device)
movements_features = torch.stack([torch.from_numpy(t) for t in ci.movements.embeds]).to(ci.device)
flavors_features = torch.stack([torch.from_numpy(t) for t in ci.flavors.embeds]).to(ci.device)
def interrogate(image):
caption = ci.generate_caption(image)
feat = ci.image_to_features(image)
medium = ci.mediums.labels[cos(feat, mediums_features).topk(1).indices[0]]
movement = ci.movements.labels[cos(feat, movements_features).topk(1).indices[0]]
flavors = ", ".join([ci.flavors.labels[i] for i in cos(feat, flavors_features).topk(3).indices])
return f"{caption}, {medium}, {movement}, {flavors}"
clip_interrogator