config # get the data mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 x_train, y_train = x_train[::5], y_train[::5] x_test, y_test = x_test[::20], y_test[::20] ...
Which is instance segmentation data set. Why I cannot train using [YOLOv8n-seg] but when i train using [YOLOv8n] the model manage train it and give result using same the data set given here is the error appear when using model [YOLOv8n-seg] Copy link Member glenn-jocher commented May...
Data scientists and AI developers use the Azure Machine Learning SDK for R to build and run machine learning workflows with Azure Machine Learning. Azure Machine Learning SDK for R uses the reticulate package to bind toAzure Machine Learning's Python SDK. By binding directly to Python, the Azur...
In1 Prepare the Python Environment, create a notebook instance using a built-in system image, install custom dependencies on the instance, and save the running instance as a container image. In the saved image, the installed dependency package is retained, but the data stored inhome/ma-user/...
Train SSD on Custom Dataset SSD is simple to use but inconvenient to modify codes. In this repo, I list all the files and codes needed to be changed when using a new dataset. data/VOC0712/create_data.sh data/VOC0712/create_list.sh data/VOC0712/labelmap_melon.prototxt examples/ssd/score...
Here are 101 public repositories matching this topic... Language:All Sort:Most stars RVC-Project/Retrieval-based-Voice-Conversion-WebUI Star29.6k Easily train a good VC model with voice data <= 10 mins! converterconversionvoiceaudio-analysischangervcvoice-conversionretrieve-datavcconversational-airetri...
ollama: deepseek-r1: # CUSTOM_OPENAI_MODEL_CONF # Regex with capture group for parsing model output # This example removes reasoning text, extracting final output: rgx_capture: '<think>.*</think>\s*(.*)|(.*)' deepseek-chat: # Use YAML alias to set value: chat_system_template: *...
Use sinks toloaddata from the metastore to the target system. Create ML pipelines Define ingestion jobs to get raw data to the metastore. Use transformers to clean, aggregate and extract features from the raw data in the metastore. Use sinks to train and deploy models or to send data from...
LLM-ready formats: markdown, structured data, screenshot, HTML, links, metadata The hard stuff: proxies, anti-bot mechanisms, dynamic content (js-rendered), output parsing, orchestration Customizability: exclude tags, crawl behind auth walls with custom headers, max crawl depth, etc... Media ...
fromultralyticsimportYOLO# Load a pretrained YOLO11n modelmodel=YOLO("yolo11n.pt")# Train the model on the COCO8 dataset for 100 epochstrain_results=model.train(data="coco8.yaml",# Path to dataset configuration fileepochs=100,# Number of training epochsimgsz=640,# Image size for training...