Update README.md

README.md

@@ -18,17 +18,13 @@ The model is trained using a "bipartite matching loss": one compares the predict
 
 ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/detr_architecture.png)
 
-## Intended uses & limitations
-
-You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=facebook/detr) to look for all available DETR models.
-
 ## Evaluation - Variants
 
 | Variant                                                                                               | Parameters | Size (MB) | Weight Precision | Act. Precision | IoU   | Pixel accuracy |
 |-------------------------------------------------------------------------------------------------------|-----------:|----------:|------------------|----------------|------:|---------------:|
 | [facebook/detr-resnet-50-panoptic (PyTorch)](https://huggingface.co/facebook/detr-resnet-50-panoptic) | 43M        | 172       | Float32          | Float32        | 0.393 | 0.746          |
-| [DETRResnet50SemanticSegmentationF32](DETRResnet50SemanticSegmentationF32.mlpackage)                                                                   | 43M        | 171       | Float32          | Float32        | 0.393 | 0.746          |
-| [DETRResnet50SemanticSegmentationF16](DETRResnet50SemanticSegmentationF16.mlpackage)                                                                   | 43M        | 86        | Float16          | Float16        | 0.395 | 0.746          |
+| [DETRResnet50SemanticSegmentationF32](DETRResnet50SemanticSegmentationF32.mlpackage)                  | 43M        | 171       | Float32          | Float32        | 0.393 | 0.746          |
+| [DETRResnet50SemanticSegmentationF16](DETRResnet50SemanticSegmentationF16.mlpackage)                  | 43M        | 86        | Float16          | Float16        | 0.395 | 0.746          |
 
 IoU and Pixel accuracy measured on 512 images from the COCO dataset. The ground truth labels were extracted from the panoptic segmentation annotations, transformed to semantic segmentation masks. Input images were resized so that the smaller edge equals 448, then center-cropped.