Clarifai is an artificial intelligence company that excels in visual recognition, solving real-world problems for businesses and developers alike. Founded in 2013 by Matthew Zeiler, a foremost expert in machine learning, Clarifai has been a market leader since winning the top five places in image classification at the ImageNet 2013 competition, and predicts more than 1.4 billion concepts in photos and videos every month. Clarifai’s powerful image and video recognition technology is built on the most advanced machine learning systems and made easily accessible by a clean API, empowering developers all over the world to build a new generation of intelligent applications. The company has amassed a suite of clients that range from Fortune 500 companies to startups and small development teams, including Buzzfeed, Trivago, 500px, StyleMePretty, and many more.
Hey Andrew, thanks for the clear and concise demo. I did this yesterday, and within under an hour I was able to finish this project 😎 Clarifai is a remarkable tool!!
I get this: "I'm sorry, but it seems that the context information you mentioned is missing" even though I am using: rag_agent.upload(file_path=r'bavykina-et-al-2019-turning-a-methanation-co-catalyst-into-an-in-co-methanol-producer.pdf',chunk_size= 1024, chunk_overlap= 200)
This is an absolutely incredible video. I'm currently doing an internship as an Electrical Engineering undergraduate at my university regarding using lasers to cause misclassification in image recognition Neural Networks. This video alongside your other content has been incredibly useful and mind opening for me! I hope you read this comment, and if you want to respond I'd love to ask questions.
Getting an error llama index, installed pip install llama-index-core==0.10.1. Now getting Import error:ImportError: cannot import name 'get_default_fs' from 'llama_index.core.readers.file.base' (/usr/local/lib/python3.10/dist-packages/llama_index/core/readers/file/base.py) ImportError: Unable to import PDFReader How to resolve this issue?
@@theworldsai Thanks for replying. Was this fixed on 10.3.1. I could not find the details of this release on your website. Please share the link where can I find the updates on 10.3.1
I've probably seen 1001 AI videos by now, but your videos are definitely among the very best. My master's thesis thanks you.🙂 Just one small thing, please don't be so fast, I'm already playing your videos at 60%. 😂 Don't you have to breathe? Like every 10 minutes or so? 😄 I'm just kidding, please have mercy for idiots like me. 🤗
Here is the code import streamlit as st from PIL import Image import pandas as pd import base64 from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel from clarifai_grpc.grpc.api import service_pb2_grpc stub = service_pb2_grpc.V2Stub(ClarifaiChannel.get_grpc_channel()) st.title("Classifier Demo") st.header("Step 1: Enter an API key") key = st.text_input("API Key") if key == '': st.warning("An API Key has not been entered.") st.stop() else: st.write("API Key has been uploaded.") file_data = st.file_uploader("Upload Image") if file_data == None: st.warning("File needs to be uploaded.") st.stop() else: image = Image.open(file_data) st.image(image) from clarifai_grpc.grpc.api import service_pb2, resources_pb2 from clarifai_grpc.grpc.api.status import status_code_pb2 # Create an application on Clarifai and put its ID here. YOUR_APPLICATION_ID = "???" # This is how you authenticate. metadata = (("authorization", f"Key {key}"),) request = service_pb2.PostModelOutputsRequest( # This is the model ID of a publicly available General model. You may use any other public or custom model ID. model_id="aaa03c23b3724a16a56b629203edc62c", user_app_id=resources_pb2.UserAppIDSet(app_id=YOUR_APPLICATION_ID), inputs=[ resources_pb2.Input( data=resources_pb2.Data(image=resources_pb2.Image(base64=file_data.getvalue())) ) ], ) response = stub.PostModelOutputs(request, metadata=metadata) if response.status.code != status_code_pb2.SUCCESS: print(response) raise Exception(f"Request failed, status code: {response.status}") names = [] confidences = [] for concept in response.outputs[0].data.concepts: names.append(concept.name) confidences.append(concept.value) # st.write( "%12s: %.2f" % (concept.name, concept.value)) df = pd.DataFrame({ "Concept Name" : names, "Concept Confidences" : confidences }) st.dataframe(df)
Hi, Yes you can use AI Assist for both moving images and video. Check out the documentation here: docs.clarifai.com/portal-guide/annotate/label-types/. Reach out to us in discord, if you need any help: discord.gg/NxVt2cEF8u
One of the key challenges for insurance companies is to maintain a balance between costs attached to claims assessment and processing efficiency which directly impacts client satisfaction levels. To contextualize, organize, and draw true meaning from data, insurers are turning to artificial intelligence (AI) to augment the capabilities of their business experts.
This is a great quesiton. This is because many users need to create custom models that recognize objects not recognized by a general model. You may want to create a model that recognizes specific brands or styles of shoes for example.
@@affrinsultana3285 there is a Cornell course on youtube by professor muhammad abdulfatheh named as machine learning hard ware....also an iit course on youtube named “ computer architecture for ai” ca4ai has also mentioned this pruning technique but not in detail...check out both, will help you definitely