Telegram Group & Telegram Channel
📢 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
Create:
Last Update:

📢 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)





Share with your friend now:
group-telegram.com/data_analysis_ml/3040

View MORE
Open in Telegram


Telegram | DID YOU KNOW?

Date: |

The SC urges the public to refer to the SC’s I nvestor Alert List before investing. The list contains details of unauthorised websites, investment products, companies and individuals. Members of the public who suspect that they have been approached by unauthorised firms or individuals offering schemes that promise unrealistic returns The War on Fakes channel has repeatedly attempted to push conspiracies that footage from Ukraine is somehow being falsified. One post on the channel from February 24 claimed without evidence that a widely viewed photo of a Ukrainian woman injured in an airstrike in the city of Chuhuiv was doctored and that the woman was seen in a different photo days later without injuries. The post, which has over 600,000 views, also baselessly claimed that the woman's blood was actually makeup or grape juice. These entities are reportedly operating nine Telegram channels with more than five million subscribers to whom they were making recommendations on selected listed scrips. Such recommendations induced the investors to deal in the said scrips, thereby creating artificial volume and price rise. That hurt tech stocks. For the past few weeks, the 10-year yield has traded between 1.72% and 2%, as traders moved into the bond for safety when Russia headlines were ugly—and out of it when headlines improved. Now, the yield is touching its pandemic-era high. If the yield breaks above that level, that could signal that it’s on a sustainable path higher. Higher long-dated bond yields make future profits less valuable—and many tech companies are valued on the basis of profits forecast for many years in the future. Ukrainian forces have since put up a strong resistance to the Russian troops amid the war that has left hundreds of Ukrainian civilians, including children, dead, according to the United Nations. Ukrainian and international officials have accused Russia of targeting civilian populations with shelling and bombardments.
from it


Telegram Анализ данных (Data analysis)
FROM American