Francesco Mattioli
2 months ago
parent
5519050a94
commit
3ea8656d9a
2 changed files with 617 additions and 42 deletions
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{ |
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"task": "detect", |
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"mode": "train", |
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"model": null, |
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"data": null, |
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"epochs": 100, |
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"time": null, |
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"patience": 100, |
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"batch": 16, |
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"imgsz": 640, |
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"save": true, |
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"save_period": -1, |
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"cache": false, |
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"device": null, |
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"workers": 8, |
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"project": null, |
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"name": null, |
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"exist_ok": false, |
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"pretrained": true, |
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"optimizer": "auto", |
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"verbose": true, |
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"seed": 0, |
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"deterministic": true, |
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"single_cls": false, |
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"rect": false, |
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"cos_lr": false, |
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"close_mosaic": 10, |
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"resume": false, |
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"amp": true, |
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"fraction": 1.0, |
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"profile": false, |
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"freeze": "None", |
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"multi_scale": false, |
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"overlap_mask": true, |
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"mask_ratio": 4, |
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"dropout": 0.0, |
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"val": true, |
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"split": "val", |
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"save_json": false, |
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"save_hybrid": false, |
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"conf": null, |
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"iou": 0.7, |
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"max_det": 300, |
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"half": false, |
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"dnn": false, |
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"plots": true, |
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"source": null, |
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"vid_stride": 1, |
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"stream_buffer": false, |
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"visualize": false, |
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"augment": false, |
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"agnostic_nms": false, |
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"classes": null, |
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"retina_masks": false, |
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"embed": null, |
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"show": false, |
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"save_frames": false, |
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"save_txt": false, |
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"save_conf": false, |
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"save_crop": false, |
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"show_labels": true, |
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"show_conf": true, |
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"show_boxes": true, |
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"line_width": null, |
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"export": { |
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"format": { |
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"type": "str", |
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"description": "Target format for the exported model, such as 'onnx', 'torchscript', 'tensorflow', or others, defining compatibility with various deployment environments.", |
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"default": "torchscript", |
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"choices": [ |
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"torchscript", |
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"onnx", |
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"openvino", |
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"engine", |
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"coreml", |
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"saved_model", |
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"pb", |
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"tflite", |
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"edgetpu", |
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"tfjs", |
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"paddle", |
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"ncnn" |
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] |
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}, |
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"keras": { |
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"type": "bool", |
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"description": "Enables export to Keras format for TensorFlow SavedModel, providing compatibility with TensorFlow serving and APIs.", |
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"default": false |
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}, |
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"optimize": { |
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"type": "bool", |
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"description": "Applies optimization for mobile devices when exporting to TorchScript, potentially reducing model size and improving performance.", |
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"default": false |
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}, |
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"int8": { |
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"type": "bool", |
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"description": "Activates INT8 quantization, further compressing the model and speeding up inference with minimal accuracy loss, primarily for edge devices.", |
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"default": false |
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}, |
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"dynamic": { |
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"type": "bool", |
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"description": "Allows dynamic input sizes for ONNX, TensorRT and OpenVINO exports, enhancing flexibility in handling varying image dimensions.", |
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"default": false |
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}, |
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"simplify": { |
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"type": "bool", |
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"description": "Simplifies the model graph for ONNX exports with onnxslim, potentially improving performance and compatibility.", |
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"default": true |
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}, |
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"opset": { |
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"type": "int", |
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"description": "Specifies the ONNX opset version for compatibility with different ONNX parsers and runtimes. If not set, uses the latest supported version.", |
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"default": "None" |
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}, |
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"workspace": { |
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"type": "int", |
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"description": "Sets the maximum workspace size in GiB for TensorRT optimizations, balancing memory usage and performance.", |
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"default": 4, |
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"min": 0.5, |
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"max": 16 |
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}, |
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"nms": { |
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"type": "bool", |
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"description": "Adds Non-Maximum Suppression (NMS) to the CoreML export, essential for accurate and efficient detection post-processing.", |
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"default": false |
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}, |
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"batch": { |
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"type": "int", |
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"description": "Specifies export model batch inference size or the max number of images the exported model will process concurrently in predict mode.", |
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"default": 1, |
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"min": 1, |
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"max": 64 |
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} |
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}, |
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"hyperparameters": { |
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"lr0": { |
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"type": "float", |
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"description": "Initial learning rate (i.e. SGD=1E-2, Adam=1E-3) . Adjusting this value is crucial for the optimization process, influencing how rapidly model weights are updated.", |
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"default": 0.01, |
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"min": 0.0001, |
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"max": 0.1, |
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"scale": "log" |
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}, |
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"lrf": { |
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"type": "float", |
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"description": "Final learning rate as a fraction of the initial rate = (lr0 * lrf), used in conjunction with schedulers to adjust the learning rate over time.", |
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"default": 0.01, |
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"min": 0.0001, |
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"max": 0.1, |
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"scale": "log" |
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}, |
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"momentum": { |
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"type": "float", |
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"description": "Momentum factor for SGD or beta1 for Adam optimizers, influencing the incorporation of past gradients in the current update.", |
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"default": 0.937, |
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"min": 0.0, |
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"max": 1.0 |
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}, |
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"weight_decay": { |
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"type": "float", |
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"description": "L2 regularization term, penalizing large weights to prevent overfitting.", |
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"default": 0.0005, |
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"min": 0.0, |
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"max": 0.01 |
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}, |
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"warmup_epochs": { |
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"type": "float", |
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"description": "Number of epochs for learning rate warmup, gradually increasing the learning rate from a low value to the initial learning rate to stabilize training early on.", |
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"default": 3.0, |
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"min": 0.0, |
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"max": 10.0 |
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}, |
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"warmup_momentum": { |
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"type": "float", |
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"description": "Initial momentum for warmup phase, gradually adjusting to the set momentum over the warmup period.", |
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"default": 0.8, |
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"min": 0.0, |
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"max": 1.0 |
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}, |
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"warmup_bias_lr": { |
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"type": "float", |
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"description": "Learning rate for bias parameters during the warmup phase, helping stabilize model training in the initial epochs.", |
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"default": 0.1, |
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"min": 0.0, |
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"max": 1.0 |
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}, |
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"box": { |
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"type": "float", |
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"description": "Weight of the box loss component in the loss function, influencing how much emphasis is placed on accurately predicting bounding box coordinates.", |
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"default": 7.5, |
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"min": 0.0, |
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"max": 10.0 |
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}, |
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"cls": { |
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"type": "float", |
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"description": "Weight of the classification loss in the total loss function, affecting the importance of correct class prediction relative to other components.", |
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"default": 0.5, |
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"min": 0.0, |
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"max": 1.0 |
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}, |
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"dfl": { |
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"type": "float", |
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"description": "Weight of the distribution focal loss, used in certain YOLO versions for fine-grained classification.", |
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"default": 1.5, |
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"min": 0.0, |
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"max": 2.0 |
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}, |
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"pose": { |
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"type": "float", |
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"description": "Weight of the pose loss in models trained for pose estimation, influencing the emphasis on accurately predicting pose keypoints.", |
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"default": 12.0, |
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"min": 0.0, |
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"max": 20.0 |
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}, |
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"kobj": { |
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"type": "float", |
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"description": "Weight of the keypoint objectness loss in pose estimation models, balancing detection confidence with pose accuracy.", |
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"default": 1.0, |
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"min": 0.0, |
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"max": 2.0 |
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}, |
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"label_smoothing": { |
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"type": "float", |
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"description": "Applies label smoothing, softening hard labels to a mix of the target label and a uniform distribution over labels, can improve generalization.", |
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"default": 0.0, |
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"min": 0.0, |
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"max": 0.1 |
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}, |
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"nbs": { |
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"type": "int", |
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"description": "Nominal batch size for normalization of loss.", |
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"default": 64, |
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"min": 1, |
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"max": 128 |
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} |
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}, |
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"augmentation": { |
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"hsv_h": { |
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"type": "float", |
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"description": "Adjusts the hue of the image by a fraction of the color wheel, introducing color variability. Helps the model generalize across different lighting conditions.", |
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"default": 0.015, |
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"min": 0.0, |
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"max": 1.0 |
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}, |
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"hsv_s": { |
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"type": "float", |
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"description": "Alters the saturation of the image by a fraction, affecting the intensity of colors. Useful for simulating different environmental conditions.", |
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"default": 0.7, |
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"min": 0.0, |
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"max": 1.0 |
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}, |
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"hsv_v": { |
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"type": "float", |
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"description": "Modifies the value (brightness) of the image by a fraction, helping the model to perform well under various lighting conditions.", |
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"default": 0.4, |
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"min": 0.0, |
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"max": 1.0 |
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}, |
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"degrees": { |
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"type": "float", |
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"description": "Rotates the image randomly within the specified degree range, improving the model's ability to recognize objects at various orientations.", |
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"default": 0.0, |
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"min": -180.0, |
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"max": 180.0 |
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}, |
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"translate": { |
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"type": "float", |
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"description": "Translates the image horizontally and vertically by a fraction of the image size, aiding in learning to detect partially visible objects.", |
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"default": 0.1, |
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"min": 0.0, |
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"max": 1.0 |
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}, |
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"scale": { |
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"type": "float", |
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"description": "Scales the image by a gain factor, simulating objects at different distances from the camera.", |
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"default": 0.5, |
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"min": 0.0, |
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"max": 1.0 |
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}, |
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"shear": { |
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"type": "float", |
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"description": "Shears the image by a specified degree, mimicking the effect of objects being viewed from different angles.", |
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"default": 0.0, |
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"min": -180.0, |
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"max": 180.0 |
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}, |
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"perspective": { |
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"type": "float", |
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"description": "Applies a random perspective transformation to the image, enhancing the model's ability to understand objects in 3D space.", |
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"default": 0.0, |
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"min": 0.0, |
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"max": 0.001 |
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}, |
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"flipud": { |
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"type": "float", |
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"description": "Flips the image upside down with the specified probability, increasing the data variability without affecting the object's characteristics.", |
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"default": 0.0, |
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"min": 0.0, |
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"max": 1.0 |
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}, |
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"fliplr": { |
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"type": "float", |
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"description": "Flips the image left to right with the specified probability, useful for learning symmetrical objects and increasing dataset diversity.", |
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"default": 0.5, |
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"min": 0.0, |
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"max": 1.0 |
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}, |
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"bgr": { |
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"type": "float", |
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"description": "Flips the image channels from RGB to BGR with the specified probability, useful for increasing robustness to incorrect channel ordering.", |
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"default": 0.0, |
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"min": 0.0, |
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"max": 1.0 |
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}, |
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"mosaic": { |
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"type": "float", |
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"description": "Combines four training images into one, simulating different scene compositions and object interactions. Highly effective for complex scene understanding.", |
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"default": 1.0, |
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"min": 0.0, |
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"max": 1.0 |
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}, |
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"mixup": { |
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"type": "float", |
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"description": "Blends two images and their labels, creating a composite image. Enhances the model's ability to generalize by introducing label noise and visual variability.", |
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"default": 0.0, |
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"min": 0.0, |
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"max": 1.0 |
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}, |
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"copy_paste": { |
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"type": "float", |
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"description": "Copies objects from one image and pastes them onto another, useful for increasing object instances and learning object occlusion.", |
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"default": 0.0, |
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"min": 0.0, |
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"max": 1.0 |
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}, |
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"copy_paste_mode": { |
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"type": "str", |
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"description": "Copy-Paste augmentation method selection among the options of (\"flip\", \"mixup\").", |
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"default": "flip", |
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"choices": [ |
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"flip", |
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"mixup" |
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] |
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}, |
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"auto_augment": { |
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"type": "str", |
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"description": "Automatically applies a predefined augmentation policy (randaugment, autoaugment, augmix), optimizing for classification tasks by diversifying the visual features.", |
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"default": "randaugment", |
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"choices": [ |
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"randaugment", |
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"autoaugment", |
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"augmix" |
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] |
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}, |
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"erasing": { |
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"type": "float", |
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"description": "Randomly erases a portion of the image during classification training, encouraging the model to focus on less obvious features for recognition.", |
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"default": 0.4, |
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"min": 0.0, |
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"max": 0.9 |
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}, |
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"crop_fraction": { |
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"type": "float", |
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"description": "Crops the classification image to a fraction of its size to emphasize central features and adapt to object scales, reducing background distractions.", |
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"default": 1.0, |
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"min": 0.1, |
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"max": 1.0 |
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} |
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}, |
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"cfg": null, |
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"tracker": "botsort.yaml" |
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} |
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