提示词/GPT Image 提示词/提示词详情图片提示词逼真机器学习开发者桌面此提示词生成一张高度逼真的macOS截图，展示程序员在VS Code中训练Python图像分类模型并配有实时浏览器仪表板，适用于产品样机、社交帖子和AI演示视觉。此提示词使用了参考图片立即使用点赞3浏览841收藏1保存或分享此提示词，下次可以轻松找到分享提示词提示词复制A photorealistic macOS desktop screenshot of a machine learning engineer’s workspace at night, shown straight-on with a dark blue macOS menu bar and the dock visible along the bottom. The desktop contains exactly 2 main application windows side by side. On the left, a large Visual Studio Code window in dark theme occupies about two-thirds of the screen. The VS Code project is named "VISIONCLASSIFIER" in the Explorer sidebar, with a realistic Python ML folder tree including exactly 11 visible top-level or expanded items: .venv, data, raw, processed, images, notebooks, src, utils, config.yaml, requirements.txt, README.md. Inside notebooks, show exactly 2 visible files: 01_data_exploration.ipynb and 02_model_training.ipynb. Inside src, show a realistic ML code structure with dataset.py, transforms.py, models, resnet.py, train, engine.py, trainer.py, utils.py. The editor area has exactly 4 tabs open: trainer.py, engine.py, resnet.py, config.yaml. The active tab is trainer.py. Display clean, believable Python training code for a ResNet image classification pipeline, including a class Trainer, methods train(self) and train_epoch(self, epoch: int) -> Dict[str, float], references to self.cfg.training.epochs, train_metrics, val_metrics, scheduler.step, save_checkpoint, self.model.train(), batch["image"], batch["label"], optimizer.zero_grad, criterion, loss.backward, optimizer.step, accuracy(outputs, targets, topk=(1,))[0]. Make the code sharp but naturally screen-like, with line numbers visible around lines 24 to 52. At the bottom of the VS Code window, the integrated terminal is open on the TERMINAL tab and shows realistic training logs for exactly 4 epochs in view: Epoch 12/50, Epoch 13/50, Epoch 14/50, Epoch 15/50, each with train and val lines listing Loss, Acc@1, and Acc@5, plus a final line saying a new best checkpoint was saved. Keep the numbers plausible for a successful training run, with top-1 accuracy around 0.88 to 0.91 and top-5 around 0.97 to 0.98. Include the usual VS Code status bar along the bottom with Python environment details. On the right, place exactly 1 dark-themed web browser window showing a local dashboard at localhost:8000 with the page title "VisionClassifier | Dashboard" and the app header "VisionClassifier" plus subtitle "Image Classification Model". The dashboard contains exactly 3 stacked sections. The first section is "Model Overview" with exactly 4 metric cards: Top-1 Accuracy 91.23%, Top-5 Accuracy 98.30%, Total Parameters 23.51M, Model ResNet-50. The second section is "Recent Training" with a dark line chart of accuracy over 50 epochs, showing exactly 2 colored curves labeled Train (Top-1) and Val (Top-1), both rising quickly and stabilizing around the low 90s. The third section is "Confusion Matrix" showing a 10x10 heatmap with a bright diagonal and axes labeled True Label and Predicted Label. Use subtle reflections, crisp typography, realistic UI spacing, and believable screen glow. The macOS top menu bar should show common menus like Code, File, Edit, Selection, View, Go, Run, Terminal, Window, Help on the left and system icons with the time reading Tue May 13 9:41 AM on the right. The dock should contain many recognizable app icons and feel authentic but not distracting. Overall style: ultra-realistic screenshot, professional developer workstation, polished dark mode interfaces, no stylization, no illustration, indistinguishable from a real screen capture.展开提示词
