Copy edit: reduce dash overuse in prose

README.md

@@ -20,10 +20,10 @@ tags:
 - unlabeled
 description: A 2,227-image, media-only sample of InsPLAD-det (UAV power line inspection
   imagery), built for a hands-on FiftyOne workshop on rare-class data curation. No
-  label fields are attached by design -- this is a genuine cold-start pool for practicing
+  label fields are attached by design; this is a genuine cold-start pool for practicing
   compression, embedding, seeded similarity mining, and annotation prioritization
   before ever touching a model. A deterministic 4-tier stratified sample (seed=51)
-  drawn from the full 10,561-image InsPLAD-det -- all 242 `tower id plate` images
+  drawn from the full 10,561-image InsPLAD-det; all 242 `tower id plate` images
   (the rare mining target), 562 images from 14 intact drone-flight sequences (a real
   near-duplicate wall), 1,210 images across `polymer insulator` / `glass insulator`
   / `yoke` (clean embedding clusters), and 213 long-tail images for texture.
@@ -109,7 +109,7 @@ InsPLAD Workshop Pool is a 2,227-image, **media-only** sample of
 teach a rare-class data curation workflow in FiftyOne: compress a raw image pool,
 embed it, mine a rare class from a handful of seed examples, prioritize the rest
 for annotation, fine-tune a detector, and correct its mistakes. This dataset ships
-with **zero label fields by design** -- the point of the exercise is deciding which
+with **zero label fields by design**. The point of the exercise is deciding which
 images deserve human attention before any labels exist. The images sampled into
 this pool were deliberately stratified (not randomly subsampled) so that every
 step of that workflow has something real to find: a genuine rare class, genuine
@@ -149,7 +149,7 @@ workflows without downloading the full 10,561-image, 4.2 GB source dataset.
 
 ### Out-of-Scope Use
 
-Not intended as a benchmark dataset for reporting detection accuracy -- it is a
+Not intended as a benchmark dataset for reporting detection accuracy. It is a
 deliberately non-random, stratified sample built for a specific teaching workflow,
 not an i.i.d. sample of InsPLAD-det. Any commercial use is out of scope; the
 source license (CC BY-NC 3.0) is non-commercial only. Not suitable for identifying
@@ -160,7 +160,7 @@ power line hardware only).
 
 This is a flat image dataset (`media_type = "image"`), not grouped or video, with
 **2,227 samples** and no splits or saved views. Every sample carries only
-FiftyOne's default fields -- there is no `ground_truth`, no per-sample split tag,
+FiftyOne's default fields; there is no `ground_truth`, no per-sample split tag,
 and no per-sample sampling-tier label. This is intentional: the dataset is meant
 to be loaded and explored exactly as if no prior work had been done on it.
 
@@ -169,7 +169,7 @@ to be loaded and explored exactly as if no prior work had been done on it.
 | Field | FiftyOne type | Description |
 |-------|---------------|-------------|
 | `filepath` | `StringField` | Path to the image file |
-| `tags` | list of `str` | Empty for every sample -- no split or tier tags are shipped |
+| `tags` | list of `str` | Empty for every sample; no split or tier tags are shipped |
 | `metadata` | `ImageMetadata` | Not populated (`None`) until `dataset.compute_metadata()` is run |
 
 ### `dataset.info`
@@ -179,7 +179,7 @@ to be loaded and explored exactly as if no prior work had been done on it.
     "source": "https://github.com/andreluizbvs/InsPLAD",
     "note": (
         "Media-only workshop pool sampled from InsPLAD-det. No labels "
-        "attached by design -- see 02_build_workshop_pool.py for the "
+        "attached by design; see 02_build_workshop_pool.py for the "
         "sampling manifest and heldout_ground_truth.json for the real "
         "boxes, held out until the 'close the loop' act."
     ),
@@ -197,8 +197,8 @@ to be loaded and explored exactly as if no prior work had been done on it.
 - **No tier or split metadata shipped.** Which sampling tier (rare target,
   duplicate-wall, common-class, long-tail) or original InsPLAD split (`train`/
   `val`) each image came from is recorded in `workshop_pool_manifest.json` at
-  build time, not carried into the FiftyOne dataset's fields -- again, so the
-  pool looks like a genuine unlabeled pool, not a labeled one with fields hidden.
+  build time, not carried into the FiftyOne dataset's fields. That keeps the
+  pool looking like a genuine unlabeled pool, not a labeled one with fields hidden.
 - **Whole scenes only, no crops.** Unlike the full InsPLAD-fault sub-datasets
   (cropped, near-square asset images), every image in this pool is a full UAV
   scene from InsPLAD-det. A bounding-box task only makes sense on full scenes,
@@ -220,7 +220,7 @@ to work with, at roughly a fifth of the source data's size.
 Two scripts build this pool from the original InsPLAD-det source; both are
 included verbatim below for full reproducibility.
 
-#### Step 1 -- Download InsPLAD-det from source
+#### Step 1: Download InsPLAD-det from source
 
 InsPLAD ships as a single Mendeley Data record containing three inner zips
 (`InsPLAD-det.zip`, `supervised_fault_classification.zip`,
@@ -230,7 +230,7 @@ untouched.
 
 ```python
 """Step 1: Download InsPLAD from source and extract only the detection
-(InsPLAD-det) sub-dataset -- full UAV scene images, no cropped fault/anomaly
+(InsPLAD-det) sub-dataset: full UAV scene images, no cropped fault/anomaly
 images. This workshop uses whole images only.
 
 Source: Mendeley Data, https://data.mendeley.com/datasets/5n3fjgvfyz/1
@@ -238,7 +238,7 @@ The Mendeley record ships one outer zip containing three inner zips
 (InsPLAD-det.zip, supervised_fault_classification.zip,
 unsupervised_anomaly_detection.zip). We download the outer zip (it's a
 single file on Mendeley, can't be split at the API level), but only extract
-InsPLAD-det.zip from it -- the other two are left zipped and untouched.
+InsPLAD-det.zip from it; the other two are left zipped and untouched.
 """
 import zipfile
 from pathlib import Path
@@ -276,16 +276,16 @@ def extract_det_only(outer_zip, det_dir):
     inner_zip_path.unlink()  # don't need the intermediate inner zip anymore
 ```
 
-Result: `data/InsPLAD-det/{train,val}/*.jpg` plus COCO annotation JSONs -- 10,561
+Result: `data/InsPLAD-det/{train,val}/*.jpg` plus COCO annotation JSONs: 10,561
 unique images (46 duplicate COCO `image_id` entries for the same file are a known
 quirk of the source data, resolved during staging).
 
-#### Step 2 -- Build the 4-tier stratified sample
+#### Step 2: Build the 4-tier stratified sample
 
 ```python
 """Step 2: Build the reproducible, stratified workshop pool manifest from
 InsPLAD-det's raw images. Whole scene images only, no labels attached to
-the resulting pool -- ground truth for the sampled images is saved
+the resulting pool: ground truth for the sampled images is saved
 separately in step 3, held out for the "close the loop" act.
 
 Tiers:
@@ -293,14 +293,14 @@ Tiers:
      ~1% of images, ~99% of them the dominant subject in frame.
   2. Duplicate-wall flights: N whole flights kept 100% intact, giving the
      "compress" act a real wall of near-identical drone frames to find
-     (not simulated -- these are actual contiguous DJI frame sequences).
+     (not simulated: these are actual contiguous DJI frame sequences).
   3. Common-class coverage: capped per-flight samples of `polymer insulator`,
-     `glass insulator`, `yoke` -- enough for clean embedding clusters
+     `glass insulator`, `yoke`, enough for clean embedding clusters
      without needing thousands of images per class.
   4. Long-tail texture: one image per remaining flight, so the embedding
      plot's messy middle still looks like a messy middle.
 
-Deterministic given SEED -- same manifest every run, same code whether
+Deterministic given SEED: same manifest every run, same code whether
 this runs live at the workshop or at home.
 """
 import random
@@ -354,14 +354,14 @@ Result, with `seed=51`:
 
 | Tier | What it keeps | Images |
 |---|---|---|
-| 1 -- Rare target (`tower id plate`, 100%) | every image containing the rare class | 242 |
-| 2 -- Duplicate-wall flights (14 flights, 100% intact) | real contiguous drone-frame sequences | 562 |
-| 3 -- Common-class coverage (capped per flight) | `polymer insulator` (450), `yoke` (450), `glass insulator` (310) | 1,210 |
-| 4 -- Long-tail texture (1/remaining flight) | everything else, thinly | 213 |
+| 1: Rare target (`tower id plate`, 100%) | every image containing the rare class | 242 |
+| 2: Duplicate-wall flights (14 flights, 100% intact) | real contiguous drone-frame sequences | 562 |
+| 3: Common-class coverage (capped per flight) | `polymer insulator` (450), `yoke` (450), `glass insulator` (310) | 1,210 |
+| 4: Long-tail texture (1/remaining flight) | everything else, thinly | 213 |
 | **Total** | | **2,227** |
 
 A later staging step copies these 2,227 images into a lean pool directory and
-converts their real COCO boxes to FiftyOne's relative `[x, y, w, h]` format --
+converts their real COCO boxes to FiftyOne's relative `[x, y, w, h]` format,
 but writes them to `heldout_ground_truth.json` rather than into the FiftyOne
 dataset, which is imported strictly media-only.
 
@@ -371,7 +371,7 @@ dataset, which is imported strictly media-only.
 
 The underlying images were captured by UAV (drone) during real-world inspections
 of operating power lines, at 1920x1080 resolution, under varied environmental
-conditions, orientations, and distances -- see the original
+conditions, orientations, and distances. See the original
 [InsPLAD dataset card](https://huggingface.co/datasets/harpreetsahota/InsPLAD)
 for the full collection and annotation process. This derivative pool applies no
 further transformation to the images themselves; it only selects which 2,227 of