Remove old template readme

README.md

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----
-license: apache-2.0
-pipeline_tag: mask-generation
-library_name: transformers
----
-
-CURRENT COPIED FROM SAM REPO IN PROGRESS
-
-Repository for FleXray: Flexible Full-Body X-ray Segmentation. See the [FleXray paper](https://arxiv.org/abs/0613.2000) for more information.
-
-The official training code is publicly release in this [repo](https://github.com/VictorButoi/FleXray/).
-
-## Usage
-
-For image prediction:
-
-```python
-import torch
-from sam2.sam2_image_predictor import SAM2ImagePredictor
-predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-large")
-with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
-    predictor.set_image(<your_image>)
-    masks, _, _ = predictor.predict(<input_prompts>)
-```
-
-For video prediction:
-
-```python
-import torch
-from sam2.sam2_video_predictor import SAM2VideoPredictor
-predictor = SAM2VideoPredictor.from_pretrained("facebook/sam2-hiera-large")
-with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
-    state = predictor.init_state(<your_video>)
-    # add new prompts and instantly get the output on the same frame
-    frame_idx, object_ids, masks = predictor.add_new_points_or_box(state, <your_prompts>):
-    # propagate the prompts to get masklets throughout the video
-    for frame_idx, object_ids, masks in predictor.propagate_in_video(state):
-        ...
-```
-
-Refer to the [demo notebooks](https://github.com/facebookresearch/segment-anything-2/tree/main/notebooks) for details.
-
-## Usage with 🤗 Transformers
-
-
-### Automatic Mask Generation with Pipeline
-
-SAM2 can be used for automatic mask generation to segment all objects in an image using the `mask-generation` pipeline:
-
-```python
->>> from transformers import pipeline
->>> generator = pipeline("mask-generation", model="facebook/sam2-hiera-large", device=0)
->>> image_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg"
->>> outputs = generator(image_url, points_per_batch=64)
->>> len(outputs["masks"])  # Number of masks generated
-39
-```
-
-### Basic Image Segmentation
-
-#### Single Point Click
-
-You can segment objects by providing a single point click on the object you want to segment:
-
-```python
->>> from transformers import Sam2Processor, Sam2Model
->>> import torch
->>> from PIL import Image
->>> import requests
->>> device = "cuda" if torch.cuda.is_available() else "cpu"
->>> model = Sam2Model.from_pretrained("facebook/sam2-hiera-large").to(device)
->>> processor = Sam2Processor.from_pretrained("facebook/sam2-hiera-large")
->>> image_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg"
->>> raw_image = Image.open(requests.get(image_url, stream=True).raw).convert("RGB")
->>> input_points = [[[[500, 375]]]]  # Single point click, 4 dimensions (image_dim, object_dim, point_per_object_dim, coordinates)
->>> input_labels = [[[1]]]  # 1 for positive click, 0 for negative click, 3 dimensions (image_dim, object_dim, point_label)
->>> inputs = processor(images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(device)
->>> with torch.no_grad():
-...     outputs = model(**inputs)
->>> masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])[0]
->>> # The model outputs multiple mask predictions ranked by quality score
->>> print(f"Generated {masks.shape[1]} masks with shape {masks.shape}")
-Generated 3 masks with shape torch.Size(1, 3, 1500, 2250)
-```
-
-
-### Citation
-
-To cite the paper, model, or software, please use the below:
-```
-@article{ravi2024sam2,
-  title={SAM 2: Segment Anything in Images and Videos},
-  author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\'a}r, Piotr and Feichtenhofer, Christoph},
-  journal={arXiv preprint arXiv:2408.00714},
-  url={https://arxiv.org/abs/2408.00714},
-  year={2024}
-}
-```
-