description updated

app.py

@@ -60,7 +60,7 @@ description = """
 Gradio demo for an object detection architecture, introduced in my bachelor thesis (link will be added). 
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 You can use this architecture to detect objects using textual queries. To use it, simply upload an image and enter any query you want.
-It can be a single word or a sentence. The model is trained to recognize only 80 categories from the COCO Detection 2017 dataset. 
+It can be a single word or a sentence. The model is trained to recognize only 80 categories (classes) from the COCO Detection 2017 dataset. 
 Refer to <a href="https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/">this</a> website 
 or the original <a href="https://arxiv.org/pdf/1405.0312.pdf">COCO</a> paper to see the full list of categories.
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@@ -90,11 +90,11 @@ Best results are obtained using one of these sentences, which were used during t
     </div>
 </div>
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-When the binarize option is turned off, model will output propabilities of requested {class} for each patch. When binarize option is turned on
+When the binarize option is turned off, model will output propabilities of requested {class} for each patch. When the binarize option is turned on
 the model will binarize each propability based on set eval_threshold.
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 Each input image is transformed to size 224x224 so it can be processed by ViT. During this transformation, different
-crop_modes and crop_percentages can be selected. No image is lost if crop_pct = 1.0. The model was trained using crop_mode='center' and crop_pct = 0.9. 
+crop_modes and crop_percentages can be selected. No image is lost if crop_pct = 1.0 and crop_mode='squash' or 'border'. The model was trained using crop_mode='center' and crop_pct = 0.9. 
 For explanation of different crop_modes, please refer to 
 <a href="https://github.com/huggingface/pytorch-image-models/blob/main/timm/data/transforms_factory.py">this</a> website, lines 155-172.
 """