Added README.md

README.md

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+---
+base_model: Qwen/Qwen2.5-VL-72B-Instruct
+library_name: transformers
+license: other
+tags:
+- llama-factory
+- full
+- generated_from_trainer
+pipeline_tag: video-text-to-text
+model-index:
+- name: bal_imb_cap_full_lr2e-4_epoch10.0_freezevisTrue_fps8
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+
+## Model description
+
+
+This model is a fine-tuned version of [Qwen/Qwen2.5-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct) on the current most, high-quality camera motion dataset that is publically available. This preview model is the current SOTA for classifying camera motion or being used for video-text retrieval with camera motion captions using [VQAScore](https://arxiv.org/pdf/2404.01291). Find more information about our work on our Github page for [CameraBench](https://github.com/sy77777en/CameraBench). *More updates to the benchmark and models will come in the future. Stay tuned!*
+## Intended uses & limitations
+
+ The usage is identical to a [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL) model. Our model is primarily useful for camera motion classification in videos as well as video-text retrieval (current SOTA in both tasks).
+ 
+ **A quick demo is shown below:**
+<details>
+<summary>Generative Scoring (for classification and retrieval):</summary>
+  
+```python
+# Import necessary libraries
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import torch
+
+# Load the model
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "chancharikm/qwen2.5-vl-72B-cam-motion-preview", torch_dtype="auto", device_map="auto"
+)
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-72B-Instruct")
+
+# Prepare input data
+video_path = "file:///path/to/video1.mp4"
+text_description = "the camera tilting upward"
+question = f"Does this video show \"{text_description}\"?"
+
+# Format the input for the model
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "video",
+                "video": video_path,
+                "fps": 8.0,  # Recommended FPS for optimal inference
+            },
+            {"type": "text", "text": question},
+        ],
+    }
+]
+
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+    **video_kwargs
+)
+inputs = inputs.to("cuda")
+
+# Generate with score output
+with torch.inference_mode():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=1,
+        do_sample=False,  # Use greedy decoding to get reliable logprobs
+        output_scores=True,
+        return_dict_in_generate=True
+    )
+
+# Calculate probability of "Yes" response
+scores = outputs.scores[0]
+probs = torch.nn.functional.softmax(scores, dim=-1)
+yes_token_id = processor.tokenizer.encode("Yes")[0]
+score = probs[0, yes_token_id].item()
+
+print(f"Video: {video_path}")
+print(f"Description: '{text_description}'")
+print(f"Score: {score:.4f}")
+```
+</details>
+
+<details>
+<summary>Natural Language Generation</summary>
+  
+```python
+# The model is trained on 8.0 FPS which we recommend for optimal inference
+
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+# default: Load the model on the available device(s)
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "chancharikm/qwen2.5-vl-72B-cam-motion-preview", torch_dtype="auto", device_map="auto"
+)
+
+# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
+# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+#     "chancharikm/qwen2.5-vl-72B-cam-motion-preview",
+#     torch_dtype=torch.bfloat16,
+#     attn_implementation="flash_attention_2",
+#     device_map="auto",
+# )
+
+# default processor
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-72B-Instruct")
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "video",
+                "video": "file:///path/to/video1.mp4",
+                "fps": 8.0,
+            },
+            {"type": "text", "text": "Describe the camera motion in this video."},
+        ],
+    }
+]
+
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    fps=fps,
+    padding=True,
+    return_tensors="pt",
+    **video_kwargs,
+)
+inputs = inputs.to("cuda")
+
+# Inference
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+</details>
+
+
+## Training and evaluation data
+
+Training and evaluation data can be found in our [repo](https://github.com/sy77777en/CameraBench).
+
+## ✏️ Citation
+
+If you find this repository useful for your research, please use the following.
+```
+@article{lin2025camerabench,
+  title={Towards Understanding Camera Motions in Any Video},
+  author={Lin, Zhiqiu and Cen, Siyuan and Jiang, Daniel and Karhade, Jay and Wang, Hewei and Mitra, Chancharik and Ling, Tiffany and Huang, Yuhan and Liu, Sifan and Chen, Mingyu and Zawar, Rushikesh and Bai, Xue and Du, Yilun and Gan, Chuang and Ramanan, Deva},
+  journal={arXiv preprint arXiv:2504.15376},
+  year={2025},
+}
+```