Update docs building code (#7601)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com> Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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@ -34,10 +34,10 @@ FastSAM is designed to address the limitations of the [Segment Anything Model (S
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This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes.
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|------------|---------------------|----------------------------------------------|-----------|------------|----------|--------|
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| FastSAM-s | `FastSAM-s.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
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| FastSAM-x | `FastSAM-x.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|------------|---------------------------------------------------------------------------------------------|----------------------------------------------|-----------|------------|----------|--------|
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| FastSAM-s | [FastSAM-s.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/FastSAM-s.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
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| FastSAM-x | [FastSAM-x.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/FastSAM-x.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
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## Usage Examples
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@ -20,9 +20,9 @@ MobileSAM is trained on a single GPU with a 100k dataset (1% of the original ima
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This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes.
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|------------|---------------------|----------------------------------------------|-----------|------------|----------|--------|
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| MobileSAM | `mobile_sam.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|------------|-----------------------------------------------------------------------------------------------|----------------------------------------------|-----------|------------|----------|--------|
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| MobileSAM | [mobile_sam.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/mobile_sam.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
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## Adapting from SAM to MobileSAM
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@ -63,10 +63,10 @@ This example provides simple RT-DETRR training and inference examples. For full
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This table presents the model types, the specific pre-trained weights, the tasks supported by each model, and the various modes ([Train](../modes/train.md) , [Val](../modes/val.md), [Predict](../modes/predict.md), [Export](../modes/export.md)) that are supported, indicated by ✅ emojis.
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|---------------------|---------------------|----------------------------------------|-----------|------------|----------|--------|
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| RT-DETR Large | `rtdetr-l.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
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| RT-DETR Extra-Large | `rtdetr-x.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|---------------------|-------------------------------------------------------------------------------------------|----------------------------------------|-----------|------------|----------|--------|
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| RT-DETR Large | [rtdetr-l.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/rtdetr-l.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
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| RT-DETR Extra-Large | [rtdetr-x.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/rtdetr-x.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
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## Citations and Acknowledgements
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@ -29,10 +29,10 @@ For an in-depth look at the Segment Anything Model and the SA-1B dataset, please
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This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes.
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|------------|---------------------|----------------------------------------------|-----------|------------|----------|--------|
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| SAM base | `sam_b.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
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| SAM large | `sam_l.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|------------|-------------------------------------------------------------------------------------|----------------------------------------------|-----------|------------|----------|--------|
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| SAM base | [sam_b.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/sam_b.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
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| SAM large | [sam_l.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/sam_l.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
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## How to Use SAM: Versatility and Power in Image Segmentation
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