Image Classification
LiteRT
LiteRT
ONNX
English
mobilenetv5
mobilenetv4
gemma-3
edge-ai
quantization
w4a8
int8
npu
qualcomm-snapdragon
google-tensor
geology
Instructions to use GeoStratum/quantification-MobileNetV5-V4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use GeoStratum/quantification-MobileNetV5-V4 with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download scripts/generate_workstation_plots_scale.R from GeoStratum/quantification-MobileNetV5-V4: direct link, hf CLI and curl.
- Browser
- Download file 3.22 kB
-
https://huggingface.co/GeoStratum/quantification-MobileNetV5-V4/resolve/main/scripts/generate_workstation_plots_scale.R
- Command line
-
hf download hf://GeoStratum/quantification-MobileNetV5-V4/scripts/generate_workstation_plots_scale.R
-
curl -L -o generate_workstation_plots_scale.R https://huggingface.co/GeoStratum/quantification-MobileNetV5-V4/resolve/main/scripts/generate_workstation_plots_scale.R
3.22 kB
| # ============================================================================== | |
| # R Script: Generation of Benchmark Plots for ScaleDetection on Machine 1 | |
| # Hardware: Workstation RTX 5060 vs Intel Core i3-12100F | |
| # Standardized on 100 Runs per Configuration (400 Total Inferences) | |
| # ============================================================================== | |
| if (Sys.getenv("R_HOME") == "" && dir.exists("E:/R/R/R-4.6.1")) { | |
| Sys.setenv(R_HOME = "E:/R/R/R-4.6.1") | |
| } | |
| results_base <- Sys.getenv("LITHO_RESULTS_DIR", unset = "results") | |
| if (!dir.exists(results_base) && dir.exists("E:/fine tuning/rock identifier/lithotheque-edge-models/results")) { | |
| results_base <- "E:/fine tuning/rock identifier/lithotheque-edge-models/results" | |
| } | |
| csv_file <- file.path(results_base, "ScaleDetection/machine_1/benchmark_machine_1_scale.csv") | |
| output_dir <- file.path(results_base, "ScaleDetection/machine_1") | |
| dir.create(output_dir, recursive = TRUE, showWarnings = FALSE) | |
| if (file.exists(csv_file)) { | |
| df <- read.csv(csv_file, stringsAsFactors = FALSE) | |
| df$Latency_num <- suppressWarnings(as.numeric(df$Latency_ms)) | |
| df_valid <- df[!is.na(df$Latency_num), ] | |
| png_file <- file.path(output_dir, "scaledetection_machine_1_comparison.png") | |
| png(filename = png_file, width = 1100, height = 650, res = 130) | |
| par(mar = c(5, 5, 4, 2), bg = "#FAFAFA") | |
| means <- c( | |
| mean(df_valid$Latency_num[df_valid$Format == "INT8" & df_valid$Backend == "GPU_RTX5060"]), | |
| mean(df_valid$Latency_num[df_valid$Format == "W4A8" & df_valid$Backend == "GPU_RTX5060"]), | |
| mean(df_valid$Latency_num[df_valid$Format == "INT8" & df_valid$Backend == "CPU_i3_12100F"]), | |
| mean(df_valid$Latency_num[df_valid$Format == "W4A8" & df_valid$Backend == "CPU_i3_12100F"]) | |
| ) | |
| labels <- c("GPU INT8\nRTX 5060", "GPU W4A8\nRTX 5060", "CPU INT8\ni3-12100F", "CPU W4A8\ni3-12100F") | |
| bar_colors <- c("#00b894", "#0984e3", "#d63031", "#e17055") | |
| bp <- barplot(means, names.arg = labels, col = bar_colors, border = NA, | |
| ylim = c(0, max(means) * 1.3), | |
| main = "ScaleDetection (1024x1024): Machine 1 Latency (RTX 5060 vs i3-12100F)", | |
| ylab = "Average Latency (ms / image - 100 passes)", | |
| font.main = 2, col.lab = "#333333", col.axis = "#555555") | |
| grid(col = "#E0E0E0", lty = "dotted") | |
| barplot(means, names.arg = NA, col = bar_colors, border = NA, ylim = c(0, max(means) * 1.3), yaxt = "n", xaxt = "n", add = TRUE) | |
| fps <- round(1000 / means, 1) | |
| text(bp, means + 1.2, labels = paste0(round(means, 2), " ms\n(", fps, " fps)"), font = 2, cex = 0.9, col = "#222222") | |
| abline(h = 16.67, col = "#636e72", lty = "dashed", lwd = 1.2) | |
| text(x = 1.0, y = 18.5, labels = "60 FPS Real-time Threshold (16.67 ms)", col = "#2d3436", cex = 0.85, font = 3) | |
| legend("topright", | |
| legend = c("GPU RTX 5060 Tensor Cores", "CPU i3-12100F x86 AVX2", "60 FPS Real-Time Threshold"), | |
| fill = c("#00b894", "#d63031", NA), | |
| border = c(NA, NA, NA), | |
| lty = c(NA, NA, 2), col = c(NA, NA, "#636e72"), lwd = c(NA, NA, 1.2), | |
| bg = "white", box.col = "#DDDDDD") | |
| dev.off() | |
| cat("[OK] Generated:", png_file, "\n") | |
| } else { | |
| cat("[WARNING] CSV file not found:", csv_file, "\n") | |
| } | |