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📢 Релиз Moondream 2B

Новая vision модель для эйдж девайсов

Поддерживает структурированные выводы, улучшенное понимание текста, отслежтвание взгляда.



from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image

model = AutoModelForCausalLM.from_pretrained(
"vikhyatk/moondream2",
revision="2025-01-09",
trust_remote_code=True,
# Uncomment to run on GPU.
# device_map={"": "cuda"}
)

# Captioning
print("Short caption:")
print(model.caption(image, length="short")["caption"])

print("\nNormal caption:")
for t in model.caption(image, length="normal", stream=True)["caption"]:
# Streaming generation example, supported for caption() and detect()
print(t, end="", flush=True)
print(model.caption(image, length="normal"))

# Visual Querying
print("\nVisual query: 'How many people are in the image?'")
print(model.query(image, "How many people are in the image?")["answer"])

# Object Detection
print("\nObject detection: 'face'")
objects = model.detect(image, "face")["objects"]
print(f"Found {len(objects)} face(s)")

# Pointing
print("\nPointing: 'person'")
points = model.point(image, "person")["points"]
print(f"Found {len(points)} person(s)")


https://huggingface.co/vikhyatk/moondream2


HF: https://huggingface.co/vikhyatk/moondream2

Demo: https://moondream.ai/playground

Github: https://github.com/vikhyat/moondream

@data_analysis_ml



group-telegram.com/data_analysis_ml/3040
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📢 Релиз Moondream 2B

Новая vision модель для эйдж девайсов

Поддерживает структурированные выводы, улучшенное понимание текста, отслежтвание взгляда.



from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image

model = AutoModelForCausalLM.from_pretrained(
"vikhyatk/moondream2",
revision="2025-01-09",
trust_remote_code=True,
# Uncomment to run on GPU.
# device_map={"": "cuda"}
)

# Captioning
print("Short caption:")
print(model.caption(image, length="short")["caption"])

print("\nNormal caption:")
for t in model.caption(image, length="normal", stream=True)["caption"]:
# Streaming generation example, supported for caption() and detect()
print(t, end="", flush=True)
print(model.caption(image, length="normal"))

# Visual Querying
print("\nVisual query: 'How many people are in the image?'")
print(model.query(image, "How many people are in the image?")["answer"])

# Object Detection
print("\nObject detection: 'face'")
objects = model.detect(image, "face")["objects"]
print(f"Found {len(objects)} face(s)")

# Pointing
print("\nPointing: 'person'")
points = model.point(image, "person")["points"]
print(f"Found {len(points)} person(s)")


https://huggingface.co/vikhyatk/moondream2


HF: https://huggingface.co/vikhyatk/moondream2

Demo: https://moondream.ai/playground

Github: https://github.com/vikhyat/moondream

@data_analysis_ml

BY Анализ данных (Data analysis)





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DFR Lab sent the image through Microsoft Azure's Face Verification program and found that it was "highly unlikely" that the person in the second photo was the same as the first woman. The fact-checker Logically AI also found the claim to be false. The woman, Olena Kurilo, was also captured in a video after the airstrike and shown to have the injuries. "Like the bombing of the maternity ward in Mariupol," he said, "Even before it hits the news, you see the videos on the Telegram channels." The Securities and Exchange Board of India (Sebi) had carried out a similar exercise in 2017 in a matter related to circulation of messages through WhatsApp. Telegram boasts 500 million users, who share information individually and in groups in relative security. But Telegram's use as a one-way broadcast channel — which followers can join but not reply to — means content from inauthentic accounts can easily reach large, captive and eager audiences.
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