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Refresh platform docs (#24281)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher
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13 changed files with 167 additions and 157 deletions
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@ -35,27 +35,28 @@ The Account section helps you:
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## Account Features
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| Feature | Description |
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| ------------ | -------------------------------------------------------- |
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| **Settings** | Profile, social links, emails, data region, and API keys |
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| **Plans** | Free, Pro, and Enterprise plan comparison |
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| **Billing** | Credits, payment methods, and transaction history |
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| **Teams** | Members, roles, invites, and seat management |
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| **Trash** | Recover deleted items within 30 days |
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| **Emails** | Add, remove, verify, and set primary email address |
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| **Activity** | Event log with inbox, archive, search, and undo |
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| Feature | Description |
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| ------------ | ----------------------------------------------------------- |
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| **Settings** | Profile, emails, social links, and data region |
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| **API Keys** | Generate AES-256-GCM encrypted keys for programmatic access |
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| **Plans** | Free, Pro, and Enterprise plan comparison |
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| **Billing** | Credits, payment methods, and transaction history |
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| **Teams** | Members, roles, invites, and seat management |
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| **Trash** | Recover deleted items within 30 days |
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| **Activity** | Event log with inbox, archive, search, and undo |
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## Settings Tabs
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Account management is organized into tabs within `Settings`:
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Account management is organized into six tabs within `Settings` (in order):
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| Tab | Description |
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| --------- | ---------------------------------------------------------------- |
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| `Profile` | Display name, bio, company, use case, emails, social links, keys |
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| `Plans` | Compare Free, Pro, and Enterprise plans |
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| `Billing` | Credit balance, top-up, payment methods, transactions |
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| `Teams` | Member list, roles, invites, seat allocation |
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| `Trash` | Soft-deleted projects, datasets, and models |
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| Tab | Description |
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| ---------- | ----------------------------------------------------------------------- |
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| `Profile` | Display name, bio, company, use case, emails, social links, data region |
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| `API Keys` | Create and manage API keys for remote training and programmatic access |
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| `Plans` | Compare Free, Pro, and Enterprise plans |
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| `Billing` | Credit balance, top-up, payment methods, transactions |
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| `Teams` | Member list, roles, invites, seat allocation |
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| `Trash` | Soft-deleted projects, datasets, and models (30-day recovery) |
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## Security
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@ -8,7 +8,7 @@ keywords: Ultralytics Platform, settings, profile, preferences, GDPR, data expor
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[Ultralytics Platform](https://platform.ultralytics.com) settings allow you to configure your profile, social links, workspace preferences, and manage your data with GDPR-compliant export and deletion options.
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Settings is organized into six tabs: `Profile`, `API Keys`, `Plans`, `Billing`, `Teams`, and `Trash`.
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Settings is organized into six tabs (in order): `Profile`, `API Keys`, `Plans`, `Billing`, `Teams`, and `Trash`.
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## Profile Tab
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@ -73,7 +73,7 @@ Team members share the workspace credit balance and resource limits. All members
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!!! note "Pro Plan Seat Billing"
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On the Pro plan, each team member is a paid seat at $29/month (or $290/year). Monthly credits of $30/seat are added to the shared wallet.
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On the Pro plan, each team member is a paid seat at $29/month (or $290/year, a ~17% saving). Monthly credits of $30/seat are added to the team's shared wallet at the start of every billing cycle.
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## Inviting Members
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@ -166,10 +166,9 @@ Access trash programmatically via the [REST API](../api/index.md#trash-api):
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=== "Empty Trash"
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```bash
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curl -X DELETE -H "Authorization: Bearer YOUR_API_KEY" \
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https://platform.ultralytics.com/api/trash/empty
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```
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!!! note "Browser session only"
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`DELETE /api/trash/empty` requires an authenticated browser session and cannot be called with an API key. Use the **Empty Trash** button in [**Settings > Trash**](../account/settings.md#trash-tab) instead, or permanently delete individual items via `DELETE /api/trash` (API-key compatible).
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## FAQ
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@ -538,17 +538,17 @@ GET /api/datasets/{datasetId}/images
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**Query Parameters:**
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| Parameter | Type | Description |
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| ------------------- | ------ | ------------------------------------------------------------------------------------------------------------- |
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| `split` | string | Filter by split: `train`, `val`, `test` |
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| `offset` | int | Pagination offset (default: 0) |
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| `limit` | int | Items per page (default: 50, max: 5000) |
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| `sort` | string | Sort order: `newest`, `oldest`, `name-asc`, `name-desc`, `size-asc`, `size-desc`, `labels-asc`, `labels-desc` |
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| `hasLabel` | string | Filter by label status (`true` or `false`) |
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| `hasError` | string | Filter by error status (`true` or `false`) |
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| `search` | string | Search by filename or image hash |
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| `includeThumbnails` | string | Include signed thumbnail URLs (default: `true`) |
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| `includeImageUrls` | string | Include signed full image URLs (default: `false`) |
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| Parameter | Type | Description |
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| ------------------- | ------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `split` | string | Filter by split: `train`, `val`, `test` |
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| `offset` | int | Pagination offset (default: 0) |
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| `limit` | int | Items per page (default: 50, max: 5000) |
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| `sort` | string | Sort order: `newest`, `oldest`, `name-asc`, `name-desc`, `height-asc`, `height-desc`, `width-asc`, `width-desc`, `size-asc`, `size-desc`, `labels-asc`, `labels-desc` (some disabled for >100k image datasets) |
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| `hasLabel` | string | Filter by label status (`true` or `false`) |
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| `hasError` | string | Filter by error status (`true` or `false`) |
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| `search` | string | Search by filename or image hash |
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| `includeThumbnails` | string | Include signed thumbnail URLs (default: `true`) |
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| `includeImageUrls` | string | Include signed full image URLs (default: `false`) |
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#### Get Signed Image URLs
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@ -881,7 +881,7 @@ Pre-load a model for faster first inference. Call this before running prediction
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## Training API
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Launch YOLO training on cloud GPUs (RTX 4090, A100, H100) and monitor progress in real time. See [Cloud Training documentation](../train/cloud-training.md).
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Launch YOLO training on cloud GPUs (23 GPU types from RTX 2000 Ada to B200) and monitor progress in real time. See [Cloud Training documentation](../train/cloud-training.md).
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```mermaid
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graph LR
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@ -1795,13 +1795,13 @@ POST /api/members
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!!! info "Member Roles"
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| Role | Permissions |
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| -------- | ------------------------------------------ |
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| `viewer` | Read-only access to workspace resources |
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| `editor` | Create, edit, and delete resources |
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| `admin` | Full access including member management |
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| Role | Permissions |
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| -------- | ------------------------------------------------------------------------------ |
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| `viewer` | Read-only access to workspace resources |
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| `editor` | Create, edit, and delete resources |
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| `admin` | Manage members, billing, and all resources (only assignable by the team owner) |
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See [Teams](../account/teams.md) for role details in the UI.
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The team `owner` is the creator and cannot be invited. Owner is transferred separately via [`POST /api/members/transfer-ownership`](#transfer-ownership). See [Teams](../account/teams.md) for full role details.
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### Update Member Role
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@ -121,10 +121,10 @@ graph LR
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The editor provides two annotation modes, selectable from the toolbar:
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| Mode | Description | Shortcut |
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| --------- | ------------------------------------------------------- | -------- |
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| **Draw** | Manual annotation with task-specific tools | `V` |
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| **Smart** | SAM-powered interactive annotation (detect/segment/OBB) | `S` |
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| Mode | Description | Shortcut |
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| ---------- | ----------------------------------------------------------------- | -------- |
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| **Manual** | Draw annotations with task-specific tools (all 5 task types) | `V` |
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| **Smart** | SAM or YOLO model-assisted annotation (detect, segment, OBB only) | `S` |
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## Manual Annotation Tools
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@ -179,13 +179,13 @@ Annotate poses using skeleton templates. Select a template from the toolbar, cli
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The editor includes 5 built-in templates:
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| Template | Keypoints | Description |
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| ---------- | --------- | ------------------------------------------------------------------------------------------------------------------ |
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| **Person** | 17 | [COCO human pose](../../datasets/pose/index.md) — nose, eyes, ears, shoulders, elbows, wrists, hips, knees, ankles |
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| **Hand** | 21 | MediaPipe hand landmarks — wrist, thumb, index, middle, ring, pinky joints |
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| **Face** | 68 | [iBUG 300W](https://ibug.doc.ic.ac.uk/resources/300-W/) facial landmarks — jaw, eyebrows, nose, eyes, mouth |
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| **Dog** | 18 | Animal pose — nose, head, neck, shoulders, legs, paws, tail |
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| **Box** | 4 | Corner keypoints — top-left, top-right, bottom-right, bottom-left |
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| Template | Keypoints | Description |
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| ---------- | --------- | ---------------------------------------------------------------------------------------------------------------------- |
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| **Person** | 17 | [COCO human body pose](../../datasets/pose/coco.md) — nose, eyes, ears, shoulders, elbows, wrists, hips, knees, ankles |
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| **Hand** | 21 | [Ultralytics Hand Keypoints](../../datasets/pose/hand-keypoints.md) — wrist, thumb, index, middle, ring, pinky joints |
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| **Face** | 68 | [iBUG 300W](https://ibug.doc.ic.ac.uk/resources/300-W/) facial landmarks — jaw, eyebrows, nose, eyes, mouth |
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| **Dog** | 18 | AP-10K animal pose — nose, head, neck, shoulders, tailbase, tail, and 4 legs (elbows, knees, paws) |
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| **Box** | 4 | Corner keypoints — top-left, top-right, bottom-right, bottom-left |
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@ -308,7 +308,7 @@ When Smart mode is active, a model picker appears in the toolbar. Five SAM model
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| Model | Size | Speed | Notes |
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| ----------------- | ------- | -------- | -------------------------- |
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| **SAM 2.1 Tiny** | 74.5 MB | Fastest | |
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| **SAM 2.1 Tiny** | 75 MB | Fastest | |
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| **SAM 2.1 Small** | 88 MB | Fast | |
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| **SAM 2.1 Base** | 154 MB | Moderate | |
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| **SAM 2.1 Large** | 428 MB | Slower | Most accurate of SAM 2.1 |
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@ -438,8 +438,7 @@ Efficient annotation with keyboard shortcuts:
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| ----------------------------- | ---------------------------- |
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| `Cmd/Ctrl+S` | Save annotations |
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| `Cmd/Ctrl+Z` | Undo |
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| `Cmd/Ctrl+Shift+Z` | Redo |
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| `Cmd/Ctrl+Y` | Redo (alternative) |
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| `Cmd/Ctrl+Y` | Redo |
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| `Escape` | Save / Deselect / Exit |
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| `Delete` / `Backspace` | Delete selected annotation |
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| `1-9` | Select class 1-9 |
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@ -453,10 +452,10 @@ Efficient annotation with keyboard shortcuts:
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=== "Modes"
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| Shortcut | Action |
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| -------- | ------------------ |
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| `V` | Draw mode (manual) |
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| `S` | Smart mode (SAM) |
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| Shortcut | Action |
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| -------- | ------------------------------- |
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| `V` | Manual mode (draw) |
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| `S` | Smart mode (SAM or YOLO model) |
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=== "Drawing"
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@ -490,7 +489,7 @@ Efficient annotation with keyboard shortcuts:
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The annotation editor maintains a full undo/redo history:
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- **Undo**: `Cmd/Ctrl+Z`
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- **Redo**: `Cmd/Ctrl+Shift+Z` or `Cmd/Ctrl+Y`
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- **Redo**: `Cmd/Ctrl+Y`
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History tracks:
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@ -549,8 +548,8 @@ The keyboard shortcut `1-9` quickly selects classes.
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Yes, but for best results:
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- Label all objects of your target classes in each image
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- Use the label filter set to `Unannotated` to identify unlabeled images
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- Exclude unannotated images from training configuration
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- Use the label filter set to `Unlabeled` to identify images that still need annotation
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- Unlabeled images are excluded from training; only labeled images contribute to the loss
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### Which SAM model should I use?
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@ -6,7 +6,7 @@ keywords: Ultralytics Platform, datasets, dataset management, dataset versioning
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# Datasets
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[Ultralytics Platform](https://platform.ultralytics.com) datasets provide a streamlined solution for managing your training data. After upload, the platform processes images, labels, and statistics automatically. A dataset is ready to train once processing has completed and it has at least one image in the `train` split, at least one image in either the `val` or `test` split, and at least one labeled image.
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[Ultralytics Platform](https://platform.ultralytics.com) datasets provide a streamlined solution for managing your training data. After upload, the platform processes images, labels, and statistics automatically. A dataset is ready to train once processing has completed and it has at least one image in the `train` split, at least one image in either the `val` or `test` split, at least one labeled image, and a total of at least two images.
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## Upload Dataset
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@ -234,28 +234,31 @@ Images can be sorted and filtered for efficient browsing:
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=== "Sort Options"
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| Sort | Description |
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| --------------- | -------------------- |
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| Newest | Most recently added |
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| Oldest | Earliest added |
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| Name A-Z | Alphabetical |
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| Name Z-A | Reverse alphabetical |
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| Size (smallest) | Smallest files first |
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| Size (largest) | Largest files first |
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| Most labels | Most annotations |
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| Fewest labels | Fewest annotations |
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| Sort | Description |
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| -------------------- | ---------------------------- |
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| Newest / Oldest | Upload / creation order |
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| Name A-Z / Z-A | Filename alphabetical |
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| Height ↑/↓ | Image height in pixels |
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| Width ↑/↓ | Image width in pixels |
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| Size ↑/↓ | File size on disk |
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| Annotations ↑/↓ | Annotation count per image |
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!!! note "Large Datasets"
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For datasets over 100,000 images, name / size / width / height sorts are disabled to keep the gallery responsive. Newest, oldest, and annotation-count sorts remain available.
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=== "Filters"
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| Filter | Options |
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| ---------------- | ---------------------------------- |
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| **Split filter** | Train, Val, Test, or All |
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| **Label filter** | All images, Annotated, or Unannotated |
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| **Search** | Filter images by filename |
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| Filter | Options |
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| ---------------- | ----------------------------------- |
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| **Split filter** | Train, Val, Test, or All |
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| **Label filter** | All, Labeled, or Unlabeled |
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| **Class filter** | Filter by class name |
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| **Search** | Filter images by filename |
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!!! tip "Finding Unlabeled Images"
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Use the label filter set to `Unannotated` to quickly find images that still need annotation. This is especially useful for large datasets where you want to track labeling progress.
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Use the label filter set to `Unlabeled` to quickly find images that still need annotation. This is especially useful for large datasets where you want to track labeling progress.
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### Fullscreen Viewer
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@ -344,17 +344,24 @@ Dedicated endpoints are **not subject to the Platform API rate limits**. Request
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### Request Parameters
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| Parameter | Type | Default | Description |
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| ----------- | ------ | ------- | ------------------------------ |
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| `file` | file | - | Image or video file (required) |
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| `conf` | float | 0.25 | Minimum confidence threshold |
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| `iou` | float | 0.7 | NMS IoU threshold |
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| `imgsz` | int | 640 | Input image size |
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| `normalize` | string | - | Return normalized coordinates |
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| Parameter | Type | Default | Range | Description |
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| ----------- | ------ | ------- | ---------- | -------------------------------------------------- |
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| `file` | file | - | - | Image or video file (required) |
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| `conf` | float | 0.25 | 0.01 – 1.0 | Minimum confidence threshold |
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| `iou` | float | 0.7 | 0.0 – 0.95 | NMS IoU threshold |
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| `imgsz` | int | 640 | 32 – 1280 | Input image size in pixels |
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| `normalize` | bool | false | - | Return bounding box coordinates as 0 – 1 |
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| `decimals` | int | 5 | 0 – 10 | Decimal precision for coordinate values |
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| `source` | string | - | - | Image URL or base64 string (alternative to `file`) |
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!!! tip "Video Inference"
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Dedicated endpoints accept video files in addition to images. Supported video formats (up to 100MB): ASF, AVI, GIF, M4V, MKV, MOV, MP4, MPEG, MPG, TS, WEBM, WMV. Each frame is processed individually and results are returned per frame. Supported image formats (up to 50MB): AVIF, BMP, DNG, HEIC, JP2, JPEG, JPG, MPO, PNG, TIF, TIFF, WEBP.
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Dedicated endpoints accept both images and videos via the `file` parameter.
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- **Image formats** (up to 50 MB): AVIF, BMP, DNG, HEIC, JP2, JPEG, JPG, MPO, PNG, TIF, TIFF, WEBP
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- **Video formats** (up to 100 MB): ASF, AVI, GIF, M4V, MKV, MOV, MP4, MPEG, MPG, TS, WEBM, WMV
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Each video frame is processed individually and results are returned per frame. You can also pass a public image URL or a base64-encoded image via the `source` parameter instead of `file`.
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### Response Format
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@ -103,11 +103,11 @@ Adjust detection behavior with parameters in the collapsible **Parameters** sect
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| Parameter | Range | Default | Description |
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| -------------- | -------------- | ------- | -------------------------------------- |
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| **Confidence** | 0.01-1.0 | 0.25 | Minimum confidence threshold |
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| **IoU** | 0.0-0.95 | 0.70 | NMS IoU threshold |
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| **Image Size** | 320, 640, 1280 | 640 | Input resize dimension (button toggle) |
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| Parameter | Range | Default | Description |
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| -------------- | -------------------------- | ------- | -------------------------------------------------------- |
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| **Confidence** | 0.01 – 1.0 | 0.25 | Minimum confidence threshold |
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| **IoU** | 0.0 – 0.95 | 0.7 | NMS IoU threshold |
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| **Image Size** | 320, 640, 1280 (UI toggle) | 640 | Input resize dimension (API accepts any value 32 – 1280) |
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!!! note "Auto-Rerun"
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@ -127,7 +127,7 @@ Control Non-Maximum Suppression:
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- **Higher (0.7+)**: Allow more overlapping boxes
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- **Lower (0.3-0.5)**: Merge nearby detections more aggressively
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- **Default (0.70)**: Balanced NMS behavior for most use cases
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- **Default (0.7)**: Balanced NMS behavior for most use cases
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## Deployment Predict
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@ -359,10 +359,10 @@ Common error responses:
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### Can I run inference on video?
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It depends on the inference method:
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Both inference methods accept video files:
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- **Dedicated endpoints** accept video files directly. Supported formats (up to 100MB): ASF, AVI, GIF, M4V, MKV, MOV, MP4, MPEG, MPG, TS, WEBM, WMV. Each frame is processed individually and results are returned per frame. See [dedicated endpoints](endpoints.md#request-parameters) for details.
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- **Shared inference** (`/api/models/{id}/predict`) accepts images only. For video, extract frames locally, send each frame as a separate request, and aggregate results.
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- **Dedicated endpoints** accept video files directly. Supported formats (up to 100 MB): ASF, AVI, GIF, M4V, MKV, MOV, MP4, MPEG, MPG, TS, WEBM, WMV. Each frame is processed individually and results are returned per frame. See [dedicated endpoints](endpoints.md#request-parameters) for details.
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- **Shared inference** (`/api/models/{id}/predict`) uses the same predict service and accepts the same video formats. However, the browser **Predict tab** in the UI only uploads images — use the REST API directly or a [dedicated endpoint](endpoints.md) for video workflows. The shared endpoint is also [rate-limited to 20 req/min](../api/index.md#per-api-key-limits), so dedicated endpoints are the better choice for heavy video workloads.
|
||||
|
||||
### How do I get the annotated image?
|
||||
|
||||
|
|
|
|||
|
|
@ -197,12 +197,12 @@ See [Projects](train/projects.md) for organizing models in your project.
|
|||
|
||||
Official `@ultralytics` content is pinned to the top of the Explore page. This includes:
|
||||
|
||||
| Project | Description | Models | Tasks |
|
||||
| --------------------------------- | --------------------------- | ---------------------------- | ------------------------------------ |
|
||||
| **[YOLO26](../models/yolo26.md)** | Latest January 2026 release | 25 models (all sizes, tasks) | detect, segment, pose, OBB, classify |
|
||||
| **[YOLO11](../models/yolo11.md)** | Current stable release | 10+ models | detect, segment, pose, OBB, classify |
|
||||
| **YOLOv8** | Previous generation | Various | detect, segment, pose, classify |
|
||||
| **YOLOv5** | Legacy, widely adopted | Various | detect, segment, classify |
|
||||
| Project | Description | Models | Tasks |
|
||||
| --------------------------------- | --------------------------- | ------------------------------ | ------------------------------------ |
|
||||
| **[YOLO26](../models/yolo26.md)** | Latest January 2026 release | 25 models (5 sizes × 5 tasks) | detect, segment, pose, OBB, classify |
|
||||
| **[YOLO11](../models/yolo11.md)** | Current stable release | 25 models (5 sizes × 5 tasks) | detect, segment, pose, OBB, classify |
|
||||
| **YOLOv8** | Previous generation | 20+ models (5 sizes × 4 tasks) | detect, segment, pose, classify |
|
||||
| **YOLOv5** | Legacy, widely adopted | 15+ models | detect, segment, classify |
|
||||
|
||||
Official datasets include benchmark datasets like [coco8](../datasets/detect/coco8.md) (8-image COCO subset), [VOC](../datasets/detect/voc.md), [african-wildlife](../datasets/detect/african-wildlife.md), [dota8](../datasets/obb/dota8.md), and other commonly used computer vision datasets.
|
||||
|
||||
|
|
|
|||
|
|
@ -66,15 +66,15 @@ graph LR
|
|||
| ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| **Upload** | Images (50MB), videos (1GB), and dataset files (ZIP, TAR including `.tar.gz`/`.tgz`, NDJSON) with automatic processing |
|
||||
| **Annotate** | Manual tools for all 5 task types, plus [Smart Annotation](data/annotation.md#smart-annotation) with SAM and YOLO models for detect, segment, and OBB (see [supported tasks](data/index.md#supported-tasks)) |
|
||||
| **Train** | Cloud GPUs (20 free + 3 Pro-exclusive), real-time metrics, project organization |
|
||||
| **Train** | Cloud GPUs (20 on all plans + 3 Pro/Enterprise-only: H200 NVL, H200 SXM, B200), real-time metrics, project organization |
|
||||
| **Export** | [17 deployment formats](../modes/export.md) (ONNX, TensorRT, CoreML, TFLite, etc.; see [supported formats](train/models.md#supported-formats)) |
|
||||
| **Deploy** | 43 global regions with dedicated endpoints, scale-to-zero behavior, and monitoring |
|
||||
| **Deploy** | 43 global regions with dedicated endpoints, scale-to-zero by default (single active instance), and monitoring |
|
||||
|
||||
**What you can do:**
|
||||
|
||||
- **Upload** images, videos, and dataset files to create training datasets
|
||||
- **Visualize** annotations with interactive overlays for all 5 YOLO task types (see [supported tasks](data/index.md#supported-tasks))
|
||||
- **Train** models on cloud GPUs (20 free, 23 with Pro) with real-time metrics
|
||||
- **Train** models on cloud GPUs (20 on all plans, 23 with Pro for H200 and B200) with real-time metrics
|
||||
- **Export** to [17 deployment formats](../modes/export.md) (ONNX, TensorRT, CoreML, TFLite, etc.)
|
||||
- **Deploy** to 43 global regions with one-click dedicated endpoints
|
||||
- **Monitor** training progress, deployment health, and usage metrics
|
||||
|
|
@ -126,7 +126,7 @@ graph LR
|
|||
|
||||
### Model Training
|
||||
|
||||
- **Cloud Training**: Train on cloud GPUs (20 free, 23 with [Pro](account/billing.md#plans)) with real-time metrics
|
||||
- **Cloud Training**: Train on cloud GPUs (20 on all plans, 23 with [Pro or Enterprise](account/billing.md#plans) for H200 and B200) with real-time metrics
|
||||
- **Remote Training**: Train anywhere and stream metrics to the platform (W&B-style)
|
||||
- **Project Organization**: Group related models, compare experiments, track activity
|
||||
- **17 Export Formats**: ONNX, TensorRT, CoreML, TFLite, and more (see [supported formats](train/models.md#supported-formats))
|
||||
|
|
@ -177,7 +177,7 @@ You can train models either through the web UI (cloud training) or from your own
|
|||
### Deployment
|
||||
|
||||
- **Inference Testing**: Test models directly in the browser with custom images
|
||||
- **Dedicated Endpoints**: Deploy to 43 global regions with scale-to-zero behavior
|
||||
- **Dedicated Endpoints**: Deploy to 43 global regions with scale-to-zero by default (single active instance)
|
||||
- **Monitoring**: Real-time metrics, request logs, and performance dashboards
|
||||
|
||||
```mermaid
|
||||
|
|
@ -243,17 +243,17 @@ Once deployed, call your endpoint from any language:
|
|||
|
||||
!!! info "Plan Tiers"
|
||||
|
||||
| Feature | Free | Pro ($29/mo) | Enterprise |
|
||||
| -------------------- | -------------- | ------------------- | -------------- |
|
||||
| Signup Credit | $5 / $25* | - | Custom |
|
||||
| Monthly Credit | - | $30/seat/month | Custom |
|
||||
| Models | 100 | 500 | Unlimited |
|
||||
| Concurrent Trainings | 3 | 10 | Unlimited |
|
||||
| Deployments | 3 | 10 | Unlimited |
|
||||
| Storage | 100 GB | 500 GB | Unlimited |
|
||||
| Cloud GPU Types | 20 | 23 (incl. H200/B200)| 23 |
|
||||
| Teams | - | Up to 5 members | Up to 50 |
|
||||
| Support | Community | Priority | Dedicated |
|
||||
| Feature | Free | Pro ($29/mo) | Enterprise |
|
||||
| -------------------- | -------------- | ----------------------- | -------------- |
|
||||
| Signup Credit | $5 / $25* | - | Custom |
|
||||
| Monthly Credit | - | $30/seat/month | Custom |
|
||||
| Models | 100 | 500 | Unlimited |
|
||||
| Concurrent Trainings | 3 | 10 | Unlimited |
|
||||
| Deployments | 3 | 10 | Unlimited |
|
||||
| Storage | 100 GB | 500 GB | Unlimited |
|
||||
| Cloud GPU Types | 20 | 23 (incl. H200 / B200) | 23 |
|
||||
| Teams | - | Up to 5 members | Up to 50 |
|
||||
| Support | Community | Priority | Dedicated |
|
||||
|
||||
*$5 at signup, or $25 with a verified company/work email.
|
||||
|
||||
|
|
@ -369,16 +369,16 @@ The Platform includes a full-featured annotation editor supporting:
|
|||
- **Smart Annotation**: Use [SAM 2.1](../models/sam-2.md) or [SAM 3](../models/sam-3.md) for click-based annotation, or run pretrained Ultralytics YOLO models and your own fine-tuned YOLO models from the toolbar for detect, segment, and OBB
|
||||
- **Keyboard Shortcuts**: Efficient workflows with hotkeys
|
||||
|
||||
| Shortcut | Action |
|
||||
| --------- | -------------------------- |
|
||||
| `V` | Select mode |
|
||||
| `S` | SAM smart annotation mode |
|
||||
| `A` | Auto-annotate mode |
|
||||
| `1` - `9` | Select class by number |
|
||||
| `Delete` | Delete selected annotation |
|
||||
| `Ctrl+Z` | Undo |
|
||||
| `Ctrl+Y` | Redo |
|
||||
| `Escape` | Cancel current action |
|
||||
| Shortcut | Action |
|
||||
| --------- | --------------------------------- |
|
||||
| `V` | Manual (draw) mode |
|
||||
| `S` | Smart mode (SAM) |
|
||||
| `A` | Toggle auto-apply (in Smart mode) |
|
||||
| `1` - `9` | Select class by number |
|
||||
| `Delete` | Delete selected annotation |
|
||||
| `Ctrl+Z` | Undo |
|
||||
| `Ctrl+Y` | Redo |
|
||||
| `Escape` | Save / deselect / exit |
|
||||
|
||||
See [Annotation](data/annotation.md) for the complete guide.
|
||||
|
||||
|
|
@ -412,12 +412,12 @@ See [Models Export](train/models.md#export-model), the [Export mode guide](../mo
|
|||
|
||||
### Dataset Issues
|
||||
|
||||
| Problem | Solution |
|
||||
| ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| Dataset won't process | Check file format is supported (JPEG, PNG, WebP, etc.). Max file size: images 50MB, videos 1GB, datasets 10GB on Free / 20GB on Pro / 50GB on Enterprise |
|
||||
| Missing annotations | Verify labels are in [YOLO format](../datasets/detect/index.md#ultralytics-yolo-format) with `.txt` files matching image filenames |
|
||||
| "Train split required" | Add `train/` folder to your dataset structure, or create splits in [dataset settings](data/datasets.md#filter-by-split) |
|
||||
| Class names undefined | Add a `data.yaml` file with `names:` list (see [YOLO format](../datasets/detect/index.md#ultralytics-yolo-format)), or define classes in dataset settings |
|
||||
| Problem | Solution |
|
||||
| ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| Dataset won't process | Check file format is supported (JPEG, PNG, WebP, TIFF, HEIC, AVIF, BMP, JP2, DNG, MPO for images). Max file size: images 50 MB, videos 1 GB, dataset archives 10 GB (Free) / 20 GB (Pro) / 50 GB (Enterprise) |
|
||||
| Missing annotations | Verify labels are in [YOLO format](../datasets/detect/index.md#ultralytics-yolo-format) with `.txt` files matching image filenames, or upload COCO JSON |
|
||||
| "Train split required" | Add `train/` folder to your dataset structure, or redistribute splits via the [split bar](data/datasets.md#split-redistribution) |
|
||||
| Class names undefined | Add a `data.yaml` file with `names:` list (see [YOLO format](../datasets/detect/index.md#ultralytics-yolo-format)), or define classes in the [Classes tab](data/datasets.md#classes-tab) |
|
||||
|
||||
### Training Issues
|
||||
|
||||
|
|
|
|||
|
|
@ -241,13 +241,13 @@ From your project, click `Train Model` to start cloud training.
|
|||
3. **Set Epochs**: Number of training iterations (default: 100)
|
||||
4. **Select GPU**: Choose compute resources based on your budget and model size
|
||||
|
||||
| Model | Size | Speed | Accuracy | Recommended GPU |
|
||||
| ------- | ----------- | -------- | -------- | -------------------- |
|
||||
| YOLO26n | Nano | Fastest | Good | RTX PRO 6000 (96 GB) |
|
||||
| YOLO26s | Small | Fast | Better | RTX PRO 6000 (96 GB) |
|
||||
| YOLO26m | Medium | Moderate | High | RTX PRO 6000 (96 GB) |
|
||||
| YOLO26l | Large | Slower | Higher | A100 (80 GB) |
|
||||
| YOLO26x | Extra Large | Slowest | Best | H100 (80 GB) |
|
||||
| Model | Size | Speed | Accuracy | Recommended GPU |
|
||||
| ------- | ----------- | -------- | -------- | ------------------------------------------------- |
|
||||
| YOLO26n | Nano | Fastest | Good | RTX PRO 6000 (96 GB) or RTX 4090 (24 GB) |
|
||||
| YOLO26s | Small | Fast | Better | RTX PRO 6000 (96 GB) |
|
||||
| YOLO26m | Medium | Moderate | High | RTX PRO 6000 (96 GB) |
|
||||
| YOLO26l | Large | Slower | Higher | RTX PRO 6000 (96 GB) or A100 SXM (80 GB) |
|
||||
| YOLO26x | Extra Large | Slowest | Best | H100 SXM (80 GB), H200 (141 GB), or B200 (180 GB) |
|
||||
|
||||
!!! info "GPU Selection"
|
||||
|
||||
|
|
|
|||
|
|
@ -62,12 +62,12 @@ Choose a dataset to train on (see [Datasets](../data/datasets.md)):
|
|||
|
||||
Set core training parameters:
|
||||
|
||||
| Parameter | Description | Default |
|
||||
| -------------- | --------------------------------------------------------------------------- | --------- |
|
||||
| **Epochs** | Number of training iterations | 100 |
|
||||
| **Batch Size** | Samples per iteration | -1 (auto) |
|
||||
| **Image Size** | Input resolution (320/416/512/640/1280 dropdown, or 32-4096 in YAML editor) | 640 |
|
||||
| **Run Name** | Optional name for the training run | auto |
|
||||
| Parameter | Description | Default |
|
||||
| -------------- | ------------------------------------------------------------------------------------------------ | --------- |
|
||||
| **Epochs** | Number of training iterations | 100 |
|
||||
| **Batch Size** | Samples per iteration | -1 (auto) |
|
||||
| **Image Size** | Input resolution (320/416/512/640/1280 dropdown, any multiple of 32 from 32-4096 in YAML editor) | 640 |
|
||||
| **Run Name** | Optional name for the training run | auto |
|
||||
|
||||
### Step 4: Advanced Settings (Optional)
|
||||
|
||||
|
|
@ -76,7 +76,7 @@ Expand **Advanced Settings** to access the full YAML-based parameter editor with
|
|||
| Group | Parameters |
|
||||
| ----------------------- | -------------------------------------------------------------------------------- |
|
||||
| **Learning Rate** | lr0, lrf, momentum, weight_decay, warmup_epochs, warmup_momentum, warmup_bias_lr |
|
||||
| **Optimizer** | SGD, MuSGD, Adam, AdamW, NAdam, RAdam, RMSProp, Adamax |
|
||||
| **Optimizer** | auto (default), SGD, MuSGD, Adam, AdamW, NAdam, RAdam, RMSProp, Adamax |
|
||||
| **Loss Weights** | box, cls, dfl, pose, kobj, label_smoothing |
|
||||
| **Color Augmentation** | hsv_h, hsv_s, hsv_v |
|
||||
| **Geometric Augment.** | degrees, translate, scale, shear, perspective |
|
||||
|
|
@ -109,10 +109,11 @@ Choose your GPU from Ultralytics Cloud:
|
|||
|
||||
!!! tip "GPU Selection"
|
||||
|
||||
- **RTX PRO 6000**: 96 GB Blackwell generation, recommended default for most jobs
|
||||
- **A100 SXM**: Required for large batch sizes or big models
|
||||
- **H100/H200**: Maximum performance for time-sensitive training (H200 requires [Pro or Enterprise](../account/billing.md#plans))
|
||||
- **B200**: NVIDIA Blackwell architecture for cutting-edge workloads (requires [Pro or Enterprise](../account/billing.md#plans))
|
||||
- **RTX PRO 6000**: 96 GB Blackwell, recommended default for most jobs
|
||||
- **A100 SXM**: 80 GB HBM2e — strong choice for large batch sizes or bigger models
|
||||
- **H100 PCIe / H100 SXM / H100 NVL**: 80–94 GB Hopper for time-sensitive training (available on all plans)
|
||||
- **H200 NVL / H200 SXM**: 141–143 GB Hopper — requires [Pro or Enterprise](../account/billing.md#plans)
|
||||
- **B200**: 180 GB NVIDIA Blackwell for cutting-edge workloads — requires [Pro or Enterprise](../account/billing.md#plans)
|
||||
|
||||
The dialog shows your current **balance** and a **Top Up** button. An estimated cost and duration are calculated based on your configuration (model size, dataset images, epochs, GPU speed).
|
||||
|
||||
|
|
@ -462,11 +463,11 @@ Yes, the **Train** button on dataset pages opens the training dialog with the da
|
|||
|
||||
=== "Core"
|
||||
|
||||
| Parameter | Type | Default | Range | Description |
|
||||
| -------------- | ---- | ------- | -------- | ------------------------------------ |
|
||||
| `epochs` | int | 100 | 1-10000 | Number of training epochs |
|
||||
| `batch` | int | 16 | 1-512 | Batch size |
|
||||
| `imgsz` | int | 640 | 32-4096 | Input image size |
|
||||
| Parameter | Type | Default | Range | Description |
|
||||
| -------------- | ---- | --------- | --------- | ------------------------------------------------ |
|
||||
| `epochs` | int | 100 | 1-10000 | Number of training epochs |
|
||||
| `batch` | int | -1 (auto) | -1 to 512 | Batch size (`-1` = auto-fit to available VRAM) |
|
||||
| `imgsz` | int | 640 | 32-4096 | Input image size |
|
||||
| `patience` | int | 100 | 1-1000 | Early stopping patience |
|
||||
| `seed` | int | 0 | 0-2147483647 | Random seed for reproducibility |
|
||||
| `deterministic`| bool | True | - | Deterministic training mode |
|
||||
|
|
|
|||
Loading…
Reference in a new issue